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Estimation of Groundwater Evapotranspiration Using Diurnal Groundwater Level Fluctuations under Three Vegetation Covers at the Hinterland of the Badain Jaran Desert

Estimation of Groundwater Evapotranspiration Using Diurnal Groundwater Level Fluctuations under... Hindawi Advances in Meteorology Volume 2020, Article ID 8478140, 14 pages https://doi.org/10.1155/2020/8478140 Research Article Estimation of Groundwater Evapotranspiration Using Diurnal GroundwaterLevelFluctuationsunderThreeVegetationCoversat the Hinterland of the Badain Jaran Desert Wenjia Zhang , Liqiang Zhao , Xinran Yu , Lyulyu Zhang , and Nai’ang Wang Center for Glacier and Desert Research, College of Earth and Environmental Sciences, Lanzhou University, Chengguan, Lanzhou, Gansu 73000, China Correspondence should be addressed to Nai’ang Wang; wangna1962lzu@163.com Received 1 August 2019; Revised 2 January 2020; Accepted 28 January 2020; Published 9 March 2020 Academic Editor: Panuganti C. S. Devara Copyright © 2020 Wenjia Zhang et al. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurate estimation of groundwater evapotranspiration (ET ) is the key for regional water budget balance and ecosystem restoration research in hyper-arid regions. Methods that use diurnal groundwater level (GWL) fluctuations have been applied to various ecosystems, especially in arid or semi-arid environments. In this study, groundwater monitoring devices were deployed in ten lake basins at the hinterland of the Badain Jaran Desert, and the White method was used to estimate the ET of these sites under three main vegetation covers. /e results showed that regular diurnal fluctuations in GWL occurred only at sites with vegetation coverage and that vegetation types and their growth status were the direct causes of this phenomenon. On a seasonal scale, the amplitudes of diurnal GWL fluctuations are related to vegetation phenology, and air temperature is an important factor controlling phenological amplitude differences. /e estimation results using the White method revealed that the ET rates varied among the observation sites with different vegetation types, and the months with the highest ET rates were also different among the sites. Overall, ET was 600∼900 mm at observation sites with Phragmites australis during a growing season (roughly early May to late October), 600∼650 mm in areas with Achnatherum splendens, and 500∼650 mm in areas with Nitraria tangutorum and Achnatherum splendens. Depth to water table and potential evapotranspiration jointly control the ET rates, while the influence of these two factors varied, depending on the specific vegetation conditions of each site. /is study elucidated the relationship between diurnal GWL fluctuations and vegetation in desert groundwater-recharged lake basins and expanded the application of the White method, providing a new basis for the calculation and simulation of regional water balance. desert ecosystems, it is critical to fully understand the in- 1.Introduction teractions between groundwater and vegetation and accu- In arid and semi-arid regions where precipitation is scarce, rately estimate the amount of groundwater consumed by most vegetation depends on groundwater for survival. Es- vegetation for the management of regional groundwater timation of groundwater evapotranspiration (ET ) is an resources. Conventional ET important component of regional water-balance studies. calculating methods (e.g., the eddy Vegetation growth is usually closely related to groundwater covariance method, lysimeter method, Penman model, and via complex feedback mechanisms [1], and the spatiotem- remote-sensing inversion model) either cannot directly poral variations of vegetation are largely determined by determine ET or demand high research cost to cover in groundwater availability [2, 3]. In the previous research, the large-scale study regions. Moreover, some observational depth to water table (DTWT) determined the spatial dis- methods may be too complex to carry out under harsh field tribution of riverside vegetation [4], and evapotranspiration conditions. In areas with a shallow DTWT, diurnal was highly correlated with the spatial distribution of vege- groundwater level (GWL) fluctuations can usually be ob- tation [5] in arid and semi-arid regions. /us, in hyper-arid served, which is attributed to regular water consumption by 2 Advances in Meteorology phreatophytes when other factors are negligible [6, 7]. ET spatiotemporal variations in water requirements. Further- can be estimated from diurnal GWL fluctuations, which was more, water resource management usually requires the first proposed by White [6]. /is estimation approach is thus prediction of future water demands based on the current referred to as the White method and has since been fre- ecosystem water demands under the intervention of human quently used for evapotranspiration calculation. Compared activities. /erefore, the daily dynamic characteristics of with other methods, ET calculation methods such as the GWL and the relationship between vegetation and highly complex and costly eddy covariance method, as well groundwater dynamics must be clarified first, after which a as the assumption of land surface homogeneity [8], the simple and practical estimation approach to calculate the White method has the advantages of being cost-effective, water requirements of oasis vegetation in the desert hin- relatively simple, and applicable to long-term continuous terland can be developed. Such an approach would support observations [9–11]. /ese characteristics highlight the the rational usage of water resources and provide scientific practicality of the White method for ET estimation, and evidence for the aforementioned unsolved problems. thus it has been continually developed and revised since its /erefore, this study sought to monitor the shallow proposal [12–15]. Currently, these methods have been ap- groundwater in the desert hinterland-recharged lake basins plied to various ecosystems, such as wetland environments of the hyper-arid Badain Jaran Desert to estimate the ET . [16, 17] and riverside oases in arid and semi-arid regions [11, 18, 19]. In the hyper-arid hinterland of the Badain Jaran 2.Materials and Methods Desert, vegetation growing in groundwater-recharged lake ° ° basins depends on groundwater for survival. /us, the White 2.1. Study Site. /e Badain Jaran Desert (39 04′15″∼42 12′23″N, ° ° method could be used to estimate ET in this hyper-arid 99 23′18″∼104 34′02″E) is located in the Alashan Plateau, desert lake ecosystem. western Mongolian Autonomous Region, China. It is Accurate ET estimation using the White method is roughly to the south of the ancient Juyan and Guaizi Lakes, based on an understanding of groundwater dynamics and its north of the Heli and Beida Mountains, west of the Yabrai relationship with vegetation. /e information extracted and Zong Nai Mountains, and east of the Gurinai Plain, with from diurnal GWL fluctuations is also used to study the an area of approximately 52,200 km [25]. /e study area lies interaction between groundwater and vegetation [10, 20]. within the northwestern marginal region of the East Asian For example, Engel et al. [21] observed diurnal GWL summer monsoon with a continental climate. /e summer ° ° fluctuations in wooded areas during the growing season, but and winter mean daily temperatures are 25.3 C and − 9.1 C, the neighboring grassland did not exhibit this phenomenon respectively [26]. /e mean annual precipitation is during the period. De Castro Ochoa and Reinoso [22] found ∼100 mm, which is mainly concentrated in May to Sep- that elevated temperatures caused an increase in the vege- tember and exhibits large interannual variability [27]. /e tation transpiration rate. When rising temperatures reach a mega-dunes and lakes in the desert are interdependent, and critical point, transpiration ceased due to leaf stomatal there are 110 perennial groundwater-recharged lakes, most closure, and these changes were reflected in daily GWL with an area <1 km [28]. /e groundwater recharge process fluctuations. Another study revealed that vegetation types, mainly occurs through cretaceous and tertiary sandstone, meteorological conditions, and soil properties jointly de- and the space that allows for shallow groundwater circu- termine the magnitude of diurnal GWL fluctuations [7]. lation is dominated by the quaternary gravel, fine sand, and Overall, the existing research highlights the close relation- fine silty sand [29]. ship between phreatophytes and their surrounding envi- /e Badain Jaran Desert has closed freshwater lakes, salt- ronment, but the actual field observation studies remain water lakes, and salt/brine lakes classified by the total dis- limited [23, 24]. /erefore, the dynamic characteristics of solved solids content in the water. /e vegetation landscape groundwater and its relationship with external conditions of the lake basin is characterized by ring zone distributions require further study. Moreover, the analysis of major around the water [30]. Waterfront regions are swampy factors affecting the ET rate would help elucidate the meadows, with a groundwater depth <0.5 m, and short and complex relationship between groundwater and vegetation. dense vegetation including species such as Triglochin mar- /e formation mechanism of the 110 permanent lakes at itima and Glaux maritima. /e second belt around the water the hinterland of the Badain Jaran Desert in northwestern is mostly saline meadow, with a groundwater depth of ∼1 m China is controversial, and the uncertainty of water con- and vegetation featuring Achnatherum splendens, Phrag- sumption in the lake basin is the primary cause of this mites australis, and Glycyrrhiza uralensis. /e outer belt has dispute. /e lake basins in the desert hinterland have a a groundwater depth of ∼2 m and vegetation cover com- considerable area with shallow groundwater, where vege- prising Nitraria tangutorum and Artemisia salsoloides. /e tation flourishes during approximately half of the year. outermost edge of vegetation is distributed among fixed and /erefore, ET cannot be neglected and is a critical com- semifixed sand dunes, connected to quicksand. /e lake ponent of the water balance calculation in the region. ecosystem in the Badain Jaran Desert, with minimal human However, the desert hinterland presents an adverse envi- activities, is ideal for the study of ecohydrological processes ronment for field workers, hindering the long-term research. in hyper-arid areas. /e research team has established ten Most monitoring methods can only be performed at a single GWL monitoring sites in the desert lake basins since 2010 site for in situ observations or in a small-scale area, which (Figure 1); the present study represents the first analysis of hinders the effective monitoring of the vegetation-driven the data from these sites. Hei River Advances in Meteorology 3 60°E 100°E 140°E 100°E 101°E 102°E 103°E 104°E 40°N Russia 42°N Kazakhstan Kyrgyzstan Mongolia Tajikistan N. Korea 30°N S. Korea Nepal Pakistan 41°N Bhutan 20°N P. R. China India Badain Jaran Desert Burma South 10°N Laos 40°N China Sea Vietnam 0° 0 800 1,600 3,200 km 39°N 035 70 140km G7 G8 40°0′N G2 G5 G4 39°50′N G10 G8 G3 G6 39°40′N G9 G1 0 5 10 20 km 101°30′E 101°45′E 102°0′E 102°15′E 102°30′E Legend Soil sample Monitoring well Weather station Figure 1: Location of the study area and observation sites. 2.2. Observation and Data Processing of GWL and Meteoro- total amount of hourly data were collected; otherwise, the logical Parameters. Ten groundwater observation wells were amplitude for that day was excluded from the analysis. established in different lake basins with shallow ground- Before groundwater series were applied to the White water, which were constructed with PVC screens with a method, a median smoothing filter in MATLAB was applied diameter of 8 cm. Hourly GWL was measured with a to eliminate noise. pressure transducer (Solinst 3001; Solinst Canada Ltd., To obtain meteorological parameters, a weather station Georgetown, ON, Canada), which had a measurement ac- (MAWS-301; Vaisala, Vantaa, Finland) was established on curacy of 0.1 cm, clock accuracy of ±1 min/year, and work the flat ground between Sumujilin South and North lakes at ° ° temperature range of − 10 C to 40 C. /e DTWT was 0∼2 m the hinterland of the Badain Jaran Desert. A QMH102 at the observation wells, and the transducers were fixed at sensor (Vaisala) was used to observe temperature ( C) and ∼30 cm below the water table. /e transducer measured both relative humidity (RH, %) with an observation interval of the total pressure of the water column above the probe and 10 s and an output interval of 10 min. An NR01 net radiation the atmospheric pressure, which was revised using a Bar- sensor (Hukseflux /ermal Sensors, Delft, the Netherlands) ologger barometer (Solinst). /e daily changes in GWL in was used to observe radiation (Rg, W/m), with output every the observation wells were calculated during the growing 30 min. Precipitation (mm) was monitored with a HOBO season, which was defined as the difference between the RG3-M (Onset Computer Corp., Bourne, MA, USA) tilting maximum and minimum values within a day (amplitude in rain gauge, recorded in rain events with an accuracy of cm). Because missing data could affect the calculation, data 0.2 mm/gauge. Based on the observed data, the daily mean were only considered if more than 90% of the normal daily temperature, daily mean RH, daily Rg, and daily Shiyang River 4 Advances in Meteorology precipitation were used for subsequent analysis. Rg and dWT (6) ET (t) � r(t) − S × . G y precipitation were the total amounts in a day, and the dt temperature was the mean of values at 03 : 00, 09 : 00, 15 : 00, /e estimated uncertainty of S (specific yield) is the and 21 : 00 (China Standard Time). Moreover, daily maxi- y major factor that causes ET estimation errors [23, 34]. /e mum and minimum temperature, RH, Rg, and wind speed G simulation experiments conducted by Loheide et al. [35] to (WS, m/s) were used to calculate potential evapotranspi- estimate S demonstrated that diurnal GWL fluctuations and ration (PET) based on the Penman–Monteith (FAO56) y antecedent moisture conditions had almost no influence on method [31]. /e data involved in this study were all ob- S . Furthermore, Meyboom [36] suggested that the readily servation records for the year 2014. y available S value should be half of the standard definition for S , whereas Loheide et al. [35] thought the suggestion 2.3. Estimation of ET from the Diurnal Fluctuations in should be based on the specific situation. To obtain the S GWL. In the revised White method, Loheide improved the value of the study area, soil samples at two depth ranges calculation accuracy in areas with shallow DTWT and ET (0–0.8 m and 0.8–1.5 m below the ground) were collected. can be estimated on an hourly basis [32]. /us, this im- /e soil moisture curve of the soil samples was determined proved method will be henceforth referred to as the Loheide using a pressure plate extractor (Daiki-3404; Daiki Rika method, which was applied to estimate the ET of eight Kogyo Co., Ltd, Saitama, Japan) to obtain important pa- observation sites under various vegetation conditions. rameters used in the van Genuchten model, and the Several assumptions were analyzed and discussed to explain properties of the soil samples were analyzed using a Mas- the suitability and reduce the uncertainty of the approach. tersizer 2000 laser diffractometer (Malvern Panalytical, Changes in groundwater storage near the observation wells Malvern, UK). /e gravel (>2 mm), sand (0.0625–2 mm), silt can be represented by the changes in GWL with time (0.004–0.0625 mm), and clay (<0.004 mm) contents of the (dWT/dt). /e changes in storage are controlled by the net 0–0.8 m soil sample were 0, 98.63, 1.37, and 0% and those of inflow or outflow of nearby groundwater (r(t)[L/T]) and the 0.8–1.5 m sample were 0, 78.58, 19.10, and 2.32%, re- ET : spectively. /e van Genuchten parameters θ (unitless), θ r s − 1 (unitless), α (cm ), and n (unitless) for the 0–0.8 m sample dWT (1) S � r(t) − ET (t), y G were 0.0291, 0.3873, 0.0425, and 2.363 and those for the dt 0.8–1.5 m sample were 0.0153, 0.3732, 0.0259, and 1.753, where S is the specific yield. respectively. /e S estimation method proposed by Crosbie When ET is zero, equation (1) can be simplified as et al. [37] was used: follows: yu dWT S � S − , y yu 1− (1/n) (2) S � r(t). 1 + α z + z /2 􏽨 􏼐 􏼐􏼐 􏼑 􏼑 􏼑􏽩 dt i f (7) /e recharge rate is a function of time [33]. Loheide [32] S � θ − θ , yu s r assumed that the head of the recharge source was constant. /us, the recharge rate of an observation well can be ob- where θ is the soil saturated moisture content, θ is the s r tained from the observed water table records, as expressed in residual moisture content, z and z are the initial and final i f equation (2): DTWT, and α and n are parameters in the van Genuchten model. dWT (3) r(WT) � S . dt 3.Results and Discussion /e method assumes that the head of the recovery re- charge has a similar change trend as the observed water table 3.1. Relationship between Diurnal GWL Fluctuations and record; therefore, the trend included in the GWL can be Vegetation. From the observed water table records, regular removed as follows: diurnal GWL fluctuations were detected at eight ground- WT (t) � WT(t) − m × t − b , (4) water observation sites, except wells G1 and G2, which were DT T T almost entirely comprised of bare sand (Table 1). /e ob- where WT (t) is the detrended GWL, WT(t) is the ob- DT servation sites where the fluctuations were detected were served GWL, m is the trendline slope, and b is the T T covered with various types of vegetation (see Table 1 for trendline intercept. main vegetation types). /is phenomenon emerged from Γ(WT ) is a function of dWT /dt and WT (t), and DT DT DT May to October, which was consistent with the growing a best-fit estimate of the function can be obtained based on season of the desert lakeside vegetation at the observation the detrended GWL from 00 : 00 to 06 : 00 of the day of sites (see Figure 2(d) for wells G6 and G8). /e findings interest and the following day. /en, to obtain the recharge mentioned above indicated that the diurnal GWL fluctua- rate function, tions were related to the vegetation covering lakeside areas. On a daily scale, the diurnal GWL fluctuations exhibited r(t) � S × 􏼂Γ(WT(t)) + m 􏼃. (5) y T a characteristics pattern whereby the water table decreased Finally, ET is calculated as continuously during the daytime and rose gradually at night, G Advances in Meteorology 5 Table 1: Classification of observation sites based on diurnal groundwater level fluctuations and the main vegetation profiles near the observation sites. No diurnal Diurnal fluctuation detected fluctuation Well no. G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 Main vegetation type Bare sand PA PA PA AS AS NT and AS NT and AS NT and AS PA: Phragmites australis; AS: Achnatherum splendens; NT: Nitraria tangutorum. 900 900 890 890 10 880 880 7 870 870 –10 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 Date Date Atmospheric pressure Temperature Precipitation (a) (b) 0.00 0.70 0.24 1.17 0.45 0.36 0.84 1.26 0.90 0.48 0.98 1.35 1.35 0.60 1.12 1.44 0.72 1.80 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 Date Date G1 G6 G2 G8 (c) (d) Figure 2: Daily temperature, precipitation, atmospheric pressure, and hourly groundwater level (GWL) fluctuations at observation sites G1, G2, G6, and G8 in 2014: (a) temperature and precipitation; (b) atmospheric pressure; (c) GWL fluctuations at observation sites G1 and G2, where no regular diurnal fluctuations were detected; (d) GWL fluctuations at observation sites G6 and G8, where regular diurnal fluctuations were detected. /e insets in (c) and (d) are derived from the corresponding groundwater level from June 5 to June 8. reaching a maximum in the morning and a minimum in the that in June, showing a larger amplitude of diurnal GWL afternoon. /is diel cycle is generally thought to be induced fluctuations in July. /e meteorological conditions in July by regular daily water consumption of phreatophytes, which that drove higher vegetation transpiration (daily average ° ° essentially represents the dynamic balance between the maximum and minimum temperature: 33.5 C and 17.3 C) groundwater lateral recharge and the consumption of could account for mentioned observations. At the end of groundwater by vegetation [6, 38–40]. On a seasonal scale, November, the vegetation entered the dormant period and there were variations in diurnal GWL fluctuations at the the diurnal GWL fluctuations decreased because of the lower eight sites, which was attributed to the interaction of veg- temperature in the desert (daily average maximum and ° ° etation with the surrounding environments [1, 7, 41]. To minimum temperature: 8.9 C and − 6.7 C). further understand the relationship between the diurnal /e diurnal GWL fluctuations exhibited variations GWL fluctuations and the vegetation at the observation sites among the observation sites. As illustrated in Figure 3, the on a seasonal scale, the diurnal GWL fluctuations during amplitudes of diurnal GWL fluctuations at observation site rainless periods at observation sites G4, G6, and G9 in G4 from June to September were larger than those of ob- different months are illustrated in Figure 3. In mid-April, the servation sites G6 and G9. To have a full understanding of diurnal GWL fluctuations of the three observation sites were these variations, the amplitudes of diurnal GWL fluctuations not obvious due to the low temperature (daily average of each observation site in the vegetation growing season and ° ° maximum and minimum temperature: 20.0 C and 3.9 C) nongrowing season were plotted as separate boxplots and no germinative vegetation. Figure 3 illustrates evident (Figure 4). Observation sites G3–G10 (with vegetation diurnal GWL fluctuations from June to September when cover) had larger amplitudes of diurnal GWL fluctuations in desert vegetation was in the growth stage and large amounts the growing season, but the amplitudes in the nongrowing of groundwater were consumed. /e diurnal GWL fluctu- season were negligible. At sites G1 and G2 (the bare sand ations in July were more obvious at observation sites G6 and sites), no evident diurnal GWL fluctuations were observed in G9, although the DTWT in July was higher compared with either the vegetation growing season or the nongrowing G1 depth to Temperature (°C) water table (m) Precipitation G2 depth to (mm/d) water table (m) G6 depth to water table (m) Atmospheric pressure (cmH O) G8 depth to water table (m) 6 Advances in Meteorology 0.72 0.90 14/4/16–4/20 14/4/16–4/20 0.95 14/11/20–11/24 0.90 14/11/20–11/24 1.00 1.08 14/9/15–9/19 14/6/6–6/10 14/7/7–7/11 1.05 14/6/6–6/10 1.26 1.10 14/7/7–7/11 14/9/15–9/19 1.44 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 Hours Hours (a) (b) 1.35 1.40 14/4/16–4/20 1.45 14/11/20–11/24 1.50 14/6/6–6/10 1.55 14/7/7–7/11 1.60 1.65 14/9/15–9/19 0:00 0:00 0:00 0:00 0:00 0:00 Hours (c) Figure 3: Seasonal variations in diurnal groundwater level fluctuations at observation site (a) G4, (b) G6, (c) G9. 12 12 10 10 Diurnal fluctuation was detected 8 8 Diurnal fluctuation was not detected 6 6 2 2 0 0 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 25%–75% 25%–75% Min-Max Min-Max Median Median (a) (b) Figure 4: Comparison of boxplots of the amplitude in groundwater level (GWL) during the (a) growing season and (b) nongrowing season. /e vertical dashed line in (a) separates sites with no regular diurnal GWL fluctuations (G1 and G2) and those with fluctuations (G3–G10); no fluctuations were detected in (b). season. Furthermore, the amplitudes of diurnal GWL by Phragmites australis, were larger than those of the sites fluctuations in the growing season varied among the sites G6–G10, with Achnatherum splendens and Nitraria tangu- G3–G10, which was attributed to the vegetation type. For torum. Interestingly, site G3, also mainly covered with instance, the amplitudes at sites G4 and G5, mainly covered Phragmites australis, had a smaller amplitude of diurnal Daily change of GWL (cm) Depth to water table (m) Depth to water table (m) Daily change of GWL (cm) Depth to water table (m) Advances in Meteorology 7 GWL fluctuations than sites G4 and G5, indicating that rates of observation sites G4 and G7 were markedly higher from vegetation type was not the only factor determining the mid-July to early September than other growth stages. /is variations in diurnal GWL fluctuations. /us, climatic indicated that the optimal growth periods varied among sites conditions, soil properties, and groundwater dynamic with different vegetation types and vitality. Due to the better processes might also cause differences in water consumption vegetation coverage and growth condition of sites G4 and G7, and recharge balance, manifesting as a spatial difference in the vegetation maintained relatively good growth and con- diurnal GWL fluctuations. sumed large amounts of groundwater even when the vegetation in other sites entered their final growth stage in September. To explain the discrepancy, a previous study suggested that dif- 3.2. Estimation of ET under 3ree Vegetation Covers. ferences in the types of deciduous and nondeciduous vegetation /e White method was used to estimate the ET rate of led to different periods reaching a maximum ET [43]. /e observation sites with three vegetation covers. /e GWL vegetation in the present study comprised herbaceous and small records of observation sites G3–G10 during the vegetation shrub plants, with different vegetation coverages from those of growing season were selected to analyze the ET rate, and the study mentioned above. /us, it is logical that the un- the statistical results are summarized in Table 2. /e ET synchronized maximum daily mean ET was driven mainly by rate was associated with vegetation type, especially in the vegetation type and growth vitality. middle of the growing season. From the relationship be- tween vegetation type and the daily average ET rate, the observation sites could be grouped into three categories: the 3.3. Controlling Factors of ET Rate. Vegetation type, me- ET rate at sites G4 and G5, with well-grown Phragmites teorological parameters, and groundwater dynamics jointly australis coverage, was ∼5 mm/d; the daily average ET of led to spatiotemporal variations in the diurnal GWL fluc- observation sites G6 and G7 dominated by Achnatherum tuations at the observation sites, which ultimately was re- splendens coverage was 3∼4 mm, including site G7 with the flected in the ET . To further explore the key factors affecting best vegetation coverage in this category, where the ET rate the ET rate at the observation sites and the mechanisms that G G reached 4 mm/d; the remaining three observation sites control ET in the Badain Jaran Desert, the relationship G8–G10 were mainly covered by Nitraria tangutorum, and between ET rate with DTWTand meteorological parameters the daily average ET rate was relatively low. Overall, the was analyzed. Taking observation site G6 as an example order of groundwater consumption by the three vegetation (Figure 6), there was a significant positive correlation between covers in this study was Phragmites australis > Achnatherum ET rate and air temperature, which was consistent with the splendens > Nitraria tangutorum. /ese results were con- result that temperature was the key factor controlling the sistent with the ET rate of similar research areas. For in- seasonal-scale variations in the diurnal GWL fluctuations stance, an area covered with Salix psammophila in the Mu Us obtained from Figure 3. Moreover, the ET rate at site G6 was Desert, China, had a DTWTof 1∼1.5 m and daily ET rate of positively correlated with solar radiation but had no signif- 3∼4 mm in the growing season [42], which was similar to the icant relationship with wind speed, relative humidity, and estimates for the observation sites with Nitraria tangutorum other factors. /ese results indicated that air temperature and and Achnatherum splendens in this study. /is similarity solar radiation are the key meteorological factors controlling supports the accuracy of the estimated results in repre- the ET rate at this observation site. senting the ET rate of desert lakeside vegetation. /e relationship between the ET rate and the external G G In this study, ET rates were also related to vegetation factors at site G6 indicated that the meteorological condi- vitality. For example, site G3 with Phragmites australis, tions in the Badain Jaran Desert were among the most compared with sites G4 and G5 with similar vegetation, had critical factors controlling the ET rate. To verify this poorer vegetation conditions with sparse patches and weak conclusion, the relationship between ET rate and PET at viability. As mentioned above, the amplitudes of diurnal GWL eight observation sites (i.e., as a comprehensive measure of fluctuations at this site were narrower, which were ultimately meteorological condition) were investigated. Significant reflected in a lower ET rate than that at sites G4 and G5. positive correlations (R � 0.38∼0.54) were observed at all Other sites also showed a similar situation, which was reflected sites except G7 (Figure 7), which demonstrated the influence in the maximum daily ET rate. Observation site G3 only had of meteorological factors on ET rate. For comparison, a G G a maximum daily ET rate of 6.2mm/d, which was similar to previous study in a semi-arid area suggested that ET was G G that of observation sites with Nitraria tangutorum. In contrast, weakly positively correlated with PET (R � 0.02∼0.32) [19], the maximum daily ET rate at site G6, with Achnatherum and Lautz [23] also reported a similar correlation between splendens, reached 11.3 mm/d, which was slightly lower than PET and ET in a riparian region. In contrast, the ET G G that of the sites with well-grown Phragmites australis. observed by Yuan et al. [18] showed a significant positive On a seasonal scale, ET is controlled by vegetation correlation with PET in desert riparian forests. Mazur et al. phenology [7]. In this study, the estimated ET was higher [17] also recognized a positive correlation and estimated the from July to August than in other stages of the growing ET (i.e., interpolated ET rate) based on the significant season (Figure 5). Moreover, the period during which the relationship between the estimated ET and PET when the peak daily mean ET occurred was not perfectly consistent White method was not appropriate for ET estimation. G G among the observation sites. /e highest daily mean ET at /erefore, the correlation between the estimated ET and G G G3, G6, G9, and G10 occurred in early July, whereas the ET PET differed among the observation sites and the G 8 Advances in Meteorology Table 2: Statistics of the estimated groundwater evapotranspiration (ET ) of observation sites during the growing season in 2014, number of days used in the estimation, and the specific yield (S ). ET (mm) Well no. Number of days S . Total Daily mean Daily max. Daily min. SD G3 169 568.7 3.4 6.2 0.3 1.5 0.18 G4 158 784.9 5.0 11.0 0.1 2.6 0.21 G5 149 757.4 5.1 13.2 0.3 3.2 0.22 G6 160 566.4 3.5 11.3 0.3 1.8 0.20 G7 160 667.2 4.0 8.3 0.6 1.7 0.23 G8 182 538.0 2.9 6.0 0.2 1.3 0.22 G9 154 590.1 3.8 7.9 0.1 2.1 0.23 G10 165 494.7 3.0 5.3 0.1 1.1 0.19 2014/5/1 2014/7/1 2014/9/1 2014/11/1 Date G3 G4 (a) 2014/5/1 2014/7/1 2014/9/1 2014/11/1 Date G6 G7 (b) 2014/5/1 2014/7/1 2014/9/1 2014/11/1 Date G9 G10 (c) Figure 5: Estimated daily groundwater evapotranspiration (ET ) of observation sites: (a) G3 and G4, (b) G6 and G7, and (c) G9 and G10 with different vegetation covers. ET (mm/d) ET (mm/d) ET (mm/d) G G G Advances in Meteorology 9 8 8 y = 26.18x – 24.00 y = 0.99x – 0.85 2 2 R = 0.49 R = 0.54 6 6 4 4 2 2 0 0 0.95 1.00 1.05 1.10 1.15 128 37 46 5 DTWT (m) PET (mm/d) (a) (b) 8 8 y = 0.26x – 2.10 y = 0.01x – 0.70 2 2 R = 0.77 R = 0.28 6 6 4 4 2 2 0 0 5 10 15 20 25 30 35 120 180 240 300 360 Air temperature (°C) Solar radiation (W/m ) (c) (d) 8 8 y = –0.09x – 4.67 y = –0.02x + 4.27 2 2 R = 0.03 R = 0.02 6 6 4 4 2 2 0 0 0 510 15 20 0 10 20 30 40 50 60 70 Wind speed (m/s) Relative humidity (%) (e) (f) Figure 6: Relationship of diurnal groundwater level fluctuations with depth to water table (DTWT), potential evapotranspiration (PET), air temperature, solar radiation, wind speed, and relative humidity at observation site G6. relationship between the two should be explored before level decline resulted in a 62% reduction in ET , high- applying the estimation method by Mazur et al. [17]. lighting the impact DTWTchanges on ET . In this study, the In this study, estimated ET and PET showed relatively DTWT range varied among the observation sites, and their high correlations at most observation sites, and there were relationships with vegetation type, vegetation vitality, and few precipitation events. /erefore, the White and Mazur’s evapotranspiration rate were consistent with the trends estimation methods could be jointly used to quantify the observed by Jackson et al. [44]. For instance, observation annual groundwater consumption (see Figure 8 for the sites G4 and G5, which were mainly covered densely with estimated results at observation site G6). Observation sites Phragmites australis, had a DTWTof 0.7∼1.5 m. Meanwhile, G3, G5, G6, G8, G9, and G10 were selected for this appli- sites G8 and G9 covered sparsely with Nitraria tangutorum, cation based on the significant relationships between the had a DTWTof 1.2∼1.7 m. Correspondingly, under the same estimated ET and PET, where the total ET in the whole meteorological conditions, the ET rate of the sites with G G G growing season (total 184 days) was obtained. /e ET Phragmites australis was markedly higher than that of sites ranged from 600 to 900 mm at observation sites with with Nitraria tangutorum. Phragmites australis and varied depending on the coverage /e estimated total ET at each observation site in the conditions, and it ranged from approximately 600 to growing season was greater than the total annual precipi- 650 mm at the sites covered with Achnatherum splendens. At tation in that year (103.7 mm), but there was no difference in sites with Nitraria tangutorum and Achnatherum splendens, the DTWT at the beginning and end of the growing season. the ET ranged from 500 to 650 mm. Overall, the water level first declined and then returned to In general, the amount of groundwater available for the original level (Figure 9), indicating that groundwater was vegetation roots decreases as DTWT increases, resulting in a the primary recharge source of the lakes in this study area, negative relationship between DTWT and ET rate [44]. with only a small amount of precipitation recharge. Fur- Furthermore, Cooper et al. [45] found that a 1.6 m water thermore, the total amount of lateral recharge and ET (mm/d) ET (mm/d) G ET (mm/d) G G ET (mm/d) ET (mm/d) ET (mm/d) G G 10 Advances in Meteorology 8 12 y = 0.77x + 0.06 y = 0.89x + 1.1 R = 0.45 R = 0.19 4 6 0 0 128 37 46 5 128 37 46 5 PET (mm/d) PET (mm/d) (a) (b) 15 8 y = 1.64x – 2.1 y = 0.99x – 0.85 12 R = 0.43 R = 0.54 0 0 1428 3 5 6 7 1628 37 4 5 PET (mm/d) PET (mm/d) (c) (d) y = 0.37x + 2.35 y = 0.58x + 0.50 8 R = 0.08 2 R = 0.38 0 0 1 2345678 128 3 4567 PET (mm/d) PET (mm/d) (e) (f) y = 0.59x + 0.45 8 y = 1.12x – 1.13 R = 0.50 R = 0.48 6 4 0 0 128 3 4567 128 3 4567 PET (mm/d) PET (mm/d) (g) (h) Figure 7: Relationship between the estimated daily groundwater evapotranspiration (ET ) and potential evapotranspiration (PET) at the eight observation sites with vegetation coverage. (a) G3, Phragmites australis (DTWT: 0.74–1.05 m). (b) G4, Phragmites australis (DTWT: 0.83–1.45 m). (c) G5, Phragmites australis (DTWT: 0.75–1.45 m). (d) G6, Achnatherum splendens (DTWT: 0.97–1.15 m). (e) G7, Ach- natherum splendens (DTWT:1.19–1.55 m). (f) G8, Nitraria tangutorum and Achnatherum splendens (DTWT:1.27–1.39 m). (g) G9, Nitraria tangutorum and Achnatherum splendens (DTWT: 1.43–1.69 m). (h) G10, Nitraria tangutorum and Achnatherum splendens (DTWT: 0.86–1.05 m). precipitation recharge of groundwater was greater than the observation site G6 (Figure 6), and the water table decreased total ET . /erefore, groundwater is the main water source by ∼0.2 m from May to August, while the estimated ET G G for vegetation growth in this case, and the ET process is showed an increasing trend (Figure 9). /is seemingly dependent on groundwater recharge. However, a positive contradictory phenomenon can be explained by the fact that correlation was indicated between ET rate and DTWT at the ET rate was jointly controlled by the DTWT and PET, G G ET (mm/d) ET (mm/d) ET (mm/d) ET (mm/d) G G G G ET (mm/d) ET (mm/d) ET (mm/d) ET (mm/d) G G G G Advances in Meteorology 11 10 recharge rates change on a daily basis and there are errors in recharge rates using the constant rise rates of GWL at night; 6 accurate specific yields are not easy to attain; with the ad- vancement of high-frequency digital data collection devices, recent studies have found that transpiration of some veg- etation did not stop completely at night [5, 46]. To overcome these deficiencies, many studies proposed several revised methods. In regard to the calculation of groundwater re- charge rates, Troxell [33] questioned whether recharge rates 0 0 were constant on a daily basis. Later, scholars represented by 14/05/01 14/06/01 14/07/01 14/08/01 14/09/01 14/10/01 14/11/01 Gribovszki et al. [14] and Loheide [32] proposed new Date methods to obtain the dynamic daily recharge rates, which PET improved the ET estimation accuracy. ET Considering the basic assumptions and their limitations, ET there are some observations to be explained in the appli- Figure 8: Estimated groundwater evapotranspiration (ET ) and cation of the groundwater diurnal signal method in Badain interpolated evapotranspiration rate (ET ) based jointly on the Jaran Desert. Due to their diverse mechanisms, GWL di- white and Mazur’s methods and potential evapotranspiration urnal fluctuations can be categorized into various types [40]. (PET) at the observation site G6. /e GWL series are easily distinguished from each other except the cycles induced by atmospheric pressure and evapotranspiration, as the two have similar fluctuant shapes. 0.72 10 R = 0.49 Atmospheric pressure effects can also occur in unconfined 0.80 aquifers typically mirroring each other [47–49]. /e diurnal 0.88 cycles induced by atmospheric pressure and evapotranspi- ration are both continuous time series. However, there are 0.96 significant differences in their daily variations. /e pressure- 1.04 dominated GWL cycles are closely related to the fluctuations 1.12 of atmospheric pressure in each season, and the valley and –2 peak values of GWL correspond to the maximum and 1.20 –4 minimum air pressure throughout the day, respectively. 14/05/01 14/06/01 14/07/01 14/08/01 14/09/01 14/10/01 14/11/01 Similar to the GWL diurnal fluctuations observed by Jiang Date et al. [50], air pressure reached a maximum at approximately ET 10 : 00 am each day when the water level was the lowest. DTWT Conversely, the pressure was lowest at 3 : 00 pm when GWL Figure 9: Estimated daily groundwater evapotranspiration (ET ) reached its maximum. In the case of the Badain Jaran Desert, of observation site G6 and the depth to water table (DTWT) in the diurnal GWL fluctuations are completely different from the growing season in 2014. above type and are consistent with the ET-diurnal cycles [40]. /e second assumption in the White method (i.e., the and the effect of PETexceeded that of DTWT in that period. transpiration of vegetation at night is negligible) can be During the observed period in this study, changes in DTWT had no marked influence on the ET rate because DTWTs confirmed by comparing the results in this study with other research results in nearby desert areas. For example, Yuan were shallow (i.e., all within 2 m) and presented relatively small changes, all within 2 m. /erefore, observation sites et al. [18] thought that groundwater evapotranspiration at night was weak based on observations using the eddy co- with a wider DTWTrange can be set up in future research to further discuss the influence of DTWT on the ET rate. variance method in areas covered by desert vegetation. /e site chosen for observation was dominated by Tamarix, where Phragmites australis, Glycyrrhiza inflata, and other 3.4. Limitations and Observations on Applying the Proposed types also grow, which was rather similar to the observation sites in this study. Additionally, other studies based on the Method. /e application of the White method and its re- vised methods are based on four basic assumptions: (1) White method estimated groundwater evapotranspiration in regions of Mu Us and Gobi Deserts that were covered with reduction in GWL is induced only by vegetation; (2) the transpiration at night is weak and negligible; (3) the average vegetation [11, 42, 43]. Although vegetation transpiration at raise rates of GWL at night can represent recharge rates of night could be negligible in most cases, other studies have groundwater in a given day; (4) the specific yields are determined that transpiration at night is considerable in representative and reliable. Although the groundwater signal certain environments. /erefore, the ET estimation in methods have been widely verified and applied in studies on these cases is based on the reliability of the assumption. In vegetation evapotranspiration of arid regions, the assump- future research, it is necessary to monitor the vegetation tions still have certain limitations. /ese shortages are water consumption at night to reduce the ET estimation specifically reflected in the following scenarios: lateral GWL uncertainty. DTWT (m) PET (mm/d) ET (mm/d) ET and ET (mm/d) G G C 12 Advances in Meteorology Methods to obtain aquifer specific yields are also im- characteristics of the regular diurnal GWL fluctuations in portant in research involving the White method. Specific spatiotemporal scales and their relationship with vegeta- yields are not only related to the aquifer soil texture but also tion were analyzed. Afterwards, the ET was estimated via affected by the depth to water table, dynamic rates of the White method, and the main controlling factors of ET temporal change of GWL, and drainage time. /ere are were explored. /e observed series indicated that diurnal various methods based on factual research conditions GWL fluctuations were consistent with the vegetation aiming to obtain a representative and reliable specific yield. growing season, which was driven by groundwater con- Gribovszki [51] recently proposed a diurnal method to sumption by phreatophytes. /e amplitudes of diurnal obtain dynamic specific yield and compared it with tradi- GWL fluctuations showed marked differences among the tional specific yield estimation techniques. Moreover, sites, which was attributed to vegetation type and vitality. pumping test estimations are considered to be a relatively On a seasonal scale, the air temperature was an important low-cost and suitable method, as it is the most similar to the external factor affecting the diurnal GWL fluctuations by dynamic specific yield. While it would be difficult to im- controlling the vegetation phenology and groundwater plement a slug test at the hinterland studied herein, relatively consumption. reliable specific yield could be feasibly obtained via a pF /e estimated ET was associated with vegetation type curve (a texture-based field capacity value). However, the and exhibited a degree of association with the vegetation specific yields obtained by this method are nondynamic and growth stage. /e observation sites could be grouped into may also become a source of ET estimation errors. three categories based on the estimated results and vege- /erefore, observations on the specific yield estimation in tation type, and the ET followed the general order this study are further explained below. Phragmites australis > Achnatherum splendens > Nitraria Specific yields strongly depend on the DTWT in shallow tangutorum. In the lake basins of the Badain Jaran Desert, groundwater environments and are dynamic in reality. the vegetation grew in the area where the DTWT was within Existing research [5] combined drainage experiments and ∼2 m, and meteorological factors had a greater influence on simulation modeling methods to obtain the changes in vegetation transpiration than in other areas with higher specific yields with DTWT within 0∼2 m. When DTWT was DTWTs. Moreover, DTWT and PET are both important within 0.8 m, specific yields varied as DTWTs became factors controlling the ET rate, while their influence varied deeper. /en, there was a difference when depth is greater among observation sites. than 0.8 m, and the effect of DTWTchange on specific yields /is study further expands the geographical applicability began decrease and approached a constant value that of the White method and offers a new research paradigm for depended on soil texture. In another case, observations the study of diurnal GWL fluctuations in groundwater- demonstrated that there was no significant correlation be- dependent lakeside ecosystems. Additionally, the study re- tween specific yields and DTWT [51], for which the range of sults could become a scientific basis to solve the controversy GWL changes was thought to be small and depth com- on the formation mechanism of the lakes in the Badain Jaran pensation in groundwater was not a key issue to consider. Desert and support the sustainable usage of regional DTWTs varied from 0.8 to 1.1 m in the second case; groundwater resources. therefore, to some extent, the results of the two studies mentioned above were consistent. In the present study of the Data Availability Badain Jaran Desert, the DTWTs of each observation site was greater than 0.8 m (Figure 7) except the observation sites /e groundwater level data and meteorological data used to G3 and G5, which had DTWTs of less than 0.8 m during a support the findings of this study were supplied by the short period of the beginning and end in the growing season. Center for Glacier and Desert Research, Lanzhou University, From this perspective, the changes in DTWTs at most ob- under license and so cannot be made freely available. Re- servation sites had little effect on the specific yield estima- quests for access to these data should be made to the cor- tions. Nevertheless, the specific yield estimation in this study responding author. was nondynamic as it did not account for the dynamic GWL temporal change rates relative to a pumping test estimation Conflicts of Interest or the dynamic diurnal method. /erefore, the specific yield estimation may become a source of ET estimation errors. /e authors declare that they have no conflicts of interest. According to Gribovzxki’s comparison, the estimated spe- cific yield in this study will be slightly higher than that Acknowledgments calculated by the dynamic method. In future research, soil moisture monitoring instruments are expected to be added We gratefully acknowledge the funding from the National at groundwater observation sites to observe dynamic specific Natural Science Foundation of China (41530745 and yields and thus improve ET estimation accuracy. 41871021) and the Fundamental Research Funds for the Central University, Lanzhou University (lzujbky-2016-275). We thank Niu Zhenmin, Xu Xingbin, Liang Xiaoyan, and 4.Conclusions Wen Penghui from the Center for Glacier and Desert Re- Based on observed GWL and meteorological parameters in search, Lanzhou University, for their contribution to the the lake basins of the Badain Jaran Desert, the field work in this study. Advances in Meteorology 13 groundwater level fluctuations,” Journal of Hydrology, References vol. 349, no. 1-2, pp. 6–17, 2008. [1] F. Orellana, P. Verma, S. P. Loheide, and E. Daly, “Monitoring [15] P. Wang and S. P. Pozdniakov, “A statistical approach to and modeling water-vegetation interactions in groundwater- estimating evapotranspiration from diurnal groundwater dependent ecosystems,” Reviews of Geophysics, vol. 50, no. 3, level fluctuations,” Water Resources Research, vol. 50, no. 3, Article ID RG3003, 2012. pp. 2276–2292, 2014. [2] E. Naumburg, R. Mata-gonzalez, R. G. Hunter, T. Mclendon, [16] C. E. Hughes, J. D. Kalma, P. Binning, G. R. Willgoose, and and D. W. Martin, “Phreatophytic vegetation and ground- M. Vertzonis, “Estimating evapotranspiration for a temperate water fluctuations: a review of current research and appli- salt marsh, Newcastle, Australia,” Hydrological Processes, cation of ecosystem response modeling with an emphasis on vol. 15, no. 6, pp. 957–975, 2001. great basin vegetation,” Environmental Management, vol. 35, [17] M. L. C. Mazur, M. J. Wiley, and D. A. Wilcox, “Estimating evapotranspiration and groundwater flow from water-table no. 6, pp. 26–740, 2005. [3] X. Li, L. Lu, G. Cheng, and H. Xiao, “Quantifying landscape fluctuations for a general wetland scenario,” Ecohydrology, vol. 7, no. 2, pp. 378–390, 2014. structure of the Heihe River Basin, north-west China using [18] G. Yuan, Y. Luo, M. Shao, P. Zhang, and X. Zhu, “Evapo- FRAGSTATS,” Journal of Arid Environments, vol. 48, no. 4, transpiration and its main controlling mechanism over the pp. 521–535, 2001. desert riparian forests in the lower Tarim River Basin,” Science [4] S. P. Loheide and S. M. Gorelick, “Riparian hydroecology: a China Earth Sciences, vol. 58, no. 6, pp. 1032–1042, 2015. coupled model of the observed interactions between [19] W. F. Yue, T. J. Wang, T. E. Franz, and X. H. Chen, “Spa- groundwater flow and meadow vegetation patterning,” Water tiotemporal patterns of water table fluctuations and evapo- Resources Research, vol. 43, no. 7, Article ID W07414, 2007. transpiration induced by riparian vegetation in a semiarid [5] J. Fan, K. T. Ostergaard, A. Guyot, S. Fujiwara, and area,” Water Resources Research, vol. 52, no. 3, pp. 1948–1960, D. A. Lockington, “Estimating groundwater evapotranspira- tion by a subtropical pine plantation using diurnal water table [20] P. Wang, Y. Zhang, J. Yu, G. Fu, and F. Ao, “Vegetation fluctuations: implications from night-time water use,” Journal dynamics induced by groundwater fluctuations in the lower of Hydrology, vol. 542, pp. 679–685, 2016. Heihe River Basin, northwestern China,” Journal of Plant [6] W. N. White, “A method of estimating ground-water supplies Ecology, vol. 4, no. 1-2, pp. 77–90, 2011. based on discharge by plants and evaporation from soil: re- [21] V. Engel, E. G. Jobbagy, ´ M. Stieglitz, M. Williams, and sults of investigations in Escalante Valley, Utah,” USGS R. B. Jackson, “Hydrological consequences of Eucalyptus Numbered Series, Water Supply Paper 659-A, U.S. Govern- afforestation in the Argentine Pampas,” Water Resources ment Printing Office, Washington, DC, USA, 1932. Research, vol. 41, no. 10, Article ID W10409, 2005. [7] J. J. Butler, G. J. Kluitenberg, D. O. Whittemore et al., “A field [22] F. De Castro Ochoa and J. M. Reinoso, “Model of long-term investigation of phreatophyte-induced fluctuations in the water-table dynamics at doñana national park,” Water Re- water table,” Water Resources Research, vol. 43, no. 2, Article search, vol. 31, no. 10, pp. 2586–2596, 1997. ID W02404, 2007. [23] L. K. Lautz, “Estimating groundwater evapotranspiration [8] J. Z. Drexler, R. L. Snyder, D. Spano, and K. T. Paw U, “A rates using diurnal water-table fluctuations in a semi-arid review of models and micrometeorological methods used to riparian zone,” Hydrogeology Journal, vol. 16, no. 3, estimate wetland evapotranspiration,” Hydrological Processes, pp. 483–497, 2008. vol. 18, no. 11, pp. 2071–2101, 2004. [24] J. Fan, K. T. Oestergaard, A. Guyot, and D. A. Lockington, [9] J. Zhu, M. Young, J. Healey, R. Jasoni, and J. Osterberg, “Estimating groundwater recharge and evapotranspiration “Interference of river level changes on riparian zone evapo- from water table fluctuations under three vegetation covers in transpiration estimates from diurnal groundwater level a coastal sandy aquifer of subtropical Australia,” Journal of fluctuations,” Journal of Hydrology, vol. 403, no. 3-4, Hydrology, vol. 519, no. Part A, pp. 1120–1129, 2014. pp. 381–389, 2011. [25] J. F. Zhu, N. A. Wang, H. B. Chen, C. Y. Dong, and [10] L. Yin, Y. Zhou, S. Ge, D. Wen, E. Zhang, and J. Dong, H. A. Zhang, “Study on the boundary and the area of Badain “Comparison and modification of methods for estimating Jaran Desert based on remote sensing imagery,” Progress in evapotranspiration using diurnal groundwater level fluctua- Geography, vol. 29, no. 9, pp. 1087–1094, 2010. tions in arid and semiarid regions,” Journal of Hydrology, [26] N. Ma, N. A. Wang, Z. L. Li, X. L. Chen, J. F. Zhu, and vol. 496, pp. 9–16, 2013. C. Y. Dong, “Analysis on climate change in the northern and [11] P. Wang, S. O. Grinevsky, S. P. Pozdniakov et al., “Application southern marginal zones of the Badain Jaran Desert during of the water table fluctuation method for estimating evapo- the period 1960–2009,” Arid Zone Research, vol. 28, no. 2, transpiration at two phreatophyte-dominated sites under pp. 242–250, 2011. hyper-arid environments,” Journal of Hydrology, vol. 519, [27] N. A. Wang, N. Ma, H. B. Chen, X. L. Chen, C. Y. Dong, and no. Part B, pp. 2289–2300, 2014. Z. Y. Zhang, “A preliminary study of precipitation charac- [12] I. C. Reigner, “A method of estimating streamflow loss by teristics in the hinterland of Badain Jaran desert,” Advances in evapotranspiration from the riparian zone,” Forest Science, Water Science, vol. 24, no. 2, pp. 153–160, 2013. vol. 12, no. 2, pp. 130–139, 1966. [28] N. Wang, K. Ning, Z. Li, Y. Wang, P. Jia, and L. Ma, “Ho- [13] P. Bauer, G. /abeng, F. Stauffer, and W. Kinzelbach, “Es- locene high lake-levels and pan-lake period on Badain Jaran timation of the evapotranspiration rate from diurnal Desert,” Science China Earth Sciences, vol. 59, no. 8, groundwater level fluctuations in the Okavango Delta, Bot- pp. 1633–1641, 2016. swana,” Journal of Hydrology, vol. 288, no. 3-4, pp. 344–355, [29] X. S. Wang, X. N. Hu, X. M. Jin, L. Z. Hou, R. Y. Qian, and 2004. L. D. Wang, “Interactions between groundwater and lakes in [14] Z. Gribovszki, P. Kalicz, J. Szilagyi, ´ and M. Kucsara, “Riparian Badain Jaran Desert,” Earth Science Frontiers, vol. 21, no. 4, zone evapotranspiration estimation from diurnal pp. 91–99, 2014. 14 Advances in Meteorology [30] Z. D. Zhu, Z. Wu, and S. Liu, An Introduction to Chinese [46] M. A. Caird, J. H. Richards, and L. A. Donovan, “Nighttime Deserts, Science Press, Beijing, China, 1980. stomatal conductance and transpiration in C3 and C4 plants,” [31] R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, “Crop Plant Physiology, vol. 143, no. 1, pp. 4–10, 2007. evapotranspiration: guidelines for computing crop water [47] P. W. Hare and R. E. Morse, “Water-level fluctuations due to barometric pressure changes in an isolated portion of an requirement,” FAO Irrigation and Drainage Paper No. 56, unconfined aquifer,” Ground Water, vol. 35, no. 4, pp. 667– pp. 1–15, Food and Agriculture Organization of the United 671, 1997. Nations, Rome, Italy, 1980. [48] X. Sun, Y. Xiang, Z. Shi, X. Hu, and H. Zhang, “Sensitivity of [32] S. P. Loheide, “A method for estimating subdaily evapo- the response of well-aquifer systems to different periodic transpiration of shallow groundwater using diurnal water loadings: a comparison of two wells in Huize, China,” Journal table fluctuations,” Ecohydrology, vol.1, no.1, pp. 59–66, 2008. of Hydrology, vol. 572, pp. 121–130, 2019. [33] H. C. Troxell, “/e diurnal fluctuation in the ground-water [49] H. Zhang, Z. Shi, G. Wang, X. Sun, R. Yan, and C. Liu, “Large and flow of the santa ana river and its meaning,” Transactions, earthquake reshapes the groundwater flow system: insight American Geophysical Union, vol.17, no. 2, pp. 496–504, 1936. from the water-level response to earth tides and atmospheric [34] R. W. Healy and P. G. Cook, “Using groundwater levels to pressure in a deep well,” Water Resources Research, vol. 55, estimate recharge,” Hydrogeology Journal, vol. 10, no. 1, no. 5, pp. 4207–4219, 2019. pp. 91–109, 2002. [50] X.-W. Jiang, Z.-C. Sun, K.-Y. Zhao et al., “A method for [35] S. P. Loheide, J. J. Butler, and S. M. Gorelick, “Estimation of simultaneous estimation of groundwater evapotranspiration groundwater consumption by phreatophytes using diurnal and inflow rates in the discharge area using seasonal water water table fluctuations: a saturated-unsaturated flow as- table fluctuations,” Journal of Hydrology, vol. 548, pp. 498– sessment,” Water Resources Research, vol. 41, no. 7, Article ID 507, 2017. W07030, 2005. [51] Z. Gribovszki, “Comparison of specific-yield estimates for [36] P. Meyboom, Bulletin 139-Groundwater Studies in the As- calculating evapotranspiration from diurnal groundwater- siniboine River Drainage Basin Part II: Hydrologic Charac- level fluctuations,” Hydrogeology Journal, vol. 26, no. 3, teristics of Phreatophytic Vegetation in South-Central pp. 869–880, 2018. Saskatchewan, Department of Energy, Mines and Resources Geological Survey of Canada, Canada, 1967. [37] R. S. Crosbie, P. Binning, and J. D. Kalma, “A time series approach to inferring groundwater recharge using the water table fluctuation method,” Water Resources Research, vol. 41, no. 1, Article ID W01008, 2005. [38] A. R. Croft, “Water loss by stream surface evaporation and transpiration by riparian vegetation,” Transactions, American Geophysical Union, vol. 29, no. 2, pp. 235–239, 1948. [39] H. M. Tschinkel, “Short-term fluctuation in streamflow as related to evaporation and transpiration,” Journal of Geo- physical Research, vol. 68, no. 24, pp. 6459–6469, 1963. [40] Z. Gribovszki, J. Szilagyi, ´ and P. Kalicz, “Diurnal fluctuations in shallow groundwater levels and streamflow rates and their interpretation—a review,” Journal of Hydrology, vol. 385, no. 1–4, pp. 371–383, 2010. [41] P. Csafordi, ´ A. Szabo, ´ K. Balog, Z. Gribovszki, A. Bidlo, ´ and T. To´th, “Factors controlling the daily change in groundwater level during the growing season on the Great Hungarian Plain: a statistical approach,” Environmental Earth Sciences, vol. 76, no. 20, p. 675, 2017. [42] D.-H. Cheng, Y. Li, X. Chen, W.-K. Wang, G.-C. Hou, and C.-L. Wang, “Estimation of groundwater evaportranspiration using diurnal water table fluctuations in the Mu Us Desert, northern China,” Journal of Hydrology, vol. 490, pp. 106–113, [43] D. Cheng, J. Duan, K. Qian, L. Qi, H. Yang, and X. Chen, “Groundwater evapotranspiration under psammophilous vegetation covers in the Mu Us Sandy Land, northern China,” Journal of Arid Land, vol. 9, no. 1, pp. 98–108, 2017. [44] R. B. Jackson, J. Canadell, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze, “A global analysis of root distributions for terrestrial biomes,” Oecologia, vol. 108, no. 3, pp. 389–411, 1996. [45] D. J. Cooper, J. S. Sanderson, D. I. Stannard, and D. P. Groeneveld, “Effects of long-term water table drawdown on evapotranspiration and vegetation in an arid region phreatophyte community,” Journal of Hydrology, vol. 325, no. 1–4, pp. 21–34, 2006. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Meteorology Hindawi Publishing Corporation

Estimation of Groundwater Evapotranspiration Using Diurnal Groundwater Level Fluctuations under Three Vegetation Covers at the Hinterland of the Badain Jaran Desert

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Hindawi Publishing Corporation
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Copyright © 2020 Wenjia Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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1687-9309
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1687-9317
DOI
10.1155/2020/8478140
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Abstract

Hindawi Advances in Meteorology Volume 2020, Article ID 8478140, 14 pages https://doi.org/10.1155/2020/8478140 Research Article Estimation of Groundwater Evapotranspiration Using Diurnal GroundwaterLevelFluctuationsunderThreeVegetationCoversat the Hinterland of the Badain Jaran Desert Wenjia Zhang , Liqiang Zhao , Xinran Yu , Lyulyu Zhang , and Nai’ang Wang Center for Glacier and Desert Research, College of Earth and Environmental Sciences, Lanzhou University, Chengguan, Lanzhou, Gansu 73000, China Correspondence should be addressed to Nai’ang Wang; wangna1962lzu@163.com Received 1 August 2019; Revised 2 January 2020; Accepted 28 January 2020; Published 9 March 2020 Academic Editor: Panuganti C. S. Devara Copyright © 2020 Wenjia Zhang et al. /is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Accurate estimation of groundwater evapotranspiration (ET ) is the key for regional water budget balance and ecosystem restoration research in hyper-arid regions. Methods that use diurnal groundwater level (GWL) fluctuations have been applied to various ecosystems, especially in arid or semi-arid environments. In this study, groundwater monitoring devices were deployed in ten lake basins at the hinterland of the Badain Jaran Desert, and the White method was used to estimate the ET of these sites under three main vegetation covers. /e results showed that regular diurnal fluctuations in GWL occurred only at sites with vegetation coverage and that vegetation types and their growth status were the direct causes of this phenomenon. On a seasonal scale, the amplitudes of diurnal GWL fluctuations are related to vegetation phenology, and air temperature is an important factor controlling phenological amplitude differences. /e estimation results using the White method revealed that the ET rates varied among the observation sites with different vegetation types, and the months with the highest ET rates were also different among the sites. Overall, ET was 600∼900 mm at observation sites with Phragmites australis during a growing season (roughly early May to late October), 600∼650 mm in areas with Achnatherum splendens, and 500∼650 mm in areas with Nitraria tangutorum and Achnatherum splendens. Depth to water table and potential evapotranspiration jointly control the ET rates, while the influence of these two factors varied, depending on the specific vegetation conditions of each site. /is study elucidated the relationship between diurnal GWL fluctuations and vegetation in desert groundwater-recharged lake basins and expanded the application of the White method, providing a new basis for the calculation and simulation of regional water balance. desert ecosystems, it is critical to fully understand the in- 1.Introduction teractions between groundwater and vegetation and accu- In arid and semi-arid regions where precipitation is scarce, rately estimate the amount of groundwater consumed by most vegetation depends on groundwater for survival. Es- vegetation for the management of regional groundwater timation of groundwater evapotranspiration (ET ) is an resources. Conventional ET important component of regional water-balance studies. calculating methods (e.g., the eddy Vegetation growth is usually closely related to groundwater covariance method, lysimeter method, Penman model, and via complex feedback mechanisms [1], and the spatiotem- remote-sensing inversion model) either cannot directly poral variations of vegetation are largely determined by determine ET or demand high research cost to cover in groundwater availability [2, 3]. In the previous research, the large-scale study regions. Moreover, some observational depth to water table (DTWT) determined the spatial dis- methods may be too complex to carry out under harsh field tribution of riverside vegetation [4], and evapotranspiration conditions. In areas with a shallow DTWT, diurnal was highly correlated with the spatial distribution of vege- groundwater level (GWL) fluctuations can usually be ob- tation [5] in arid and semi-arid regions. /us, in hyper-arid served, which is attributed to regular water consumption by 2 Advances in Meteorology phreatophytes when other factors are negligible [6, 7]. ET spatiotemporal variations in water requirements. Further- can be estimated from diurnal GWL fluctuations, which was more, water resource management usually requires the first proposed by White [6]. /is estimation approach is thus prediction of future water demands based on the current referred to as the White method and has since been fre- ecosystem water demands under the intervention of human quently used for evapotranspiration calculation. Compared activities. /erefore, the daily dynamic characteristics of with other methods, ET calculation methods such as the GWL and the relationship between vegetation and highly complex and costly eddy covariance method, as well groundwater dynamics must be clarified first, after which a as the assumption of land surface homogeneity [8], the simple and practical estimation approach to calculate the White method has the advantages of being cost-effective, water requirements of oasis vegetation in the desert hin- relatively simple, and applicable to long-term continuous terland can be developed. Such an approach would support observations [9–11]. /ese characteristics highlight the the rational usage of water resources and provide scientific practicality of the White method for ET estimation, and evidence for the aforementioned unsolved problems. thus it has been continually developed and revised since its /erefore, this study sought to monitor the shallow proposal [12–15]. Currently, these methods have been ap- groundwater in the desert hinterland-recharged lake basins plied to various ecosystems, such as wetland environments of the hyper-arid Badain Jaran Desert to estimate the ET . [16, 17] and riverside oases in arid and semi-arid regions [11, 18, 19]. In the hyper-arid hinterland of the Badain Jaran 2.Materials and Methods Desert, vegetation growing in groundwater-recharged lake ° ° basins depends on groundwater for survival. /us, the White 2.1. Study Site. /e Badain Jaran Desert (39 04′15″∼42 12′23″N, ° ° method could be used to estimate ET in this hyper-arid 99 23′18″∼104 34′02″E) is located in the Alashan Plateau, desert lake ecosystem. western Mongolian Autonomous Region, China. It is Accurate ET estimation using the White method is roughly to the south of the ancient Juyan and Guaizi Lakes, based on an understanding of groundwater dynamics and its north of the Heli and Beida Mountains, west of the Yabrai relationship with vegetation. /e information extracted and Zong Nai Mountains, and east of the Gurinai Plain, with from diurnal GWL fluctuations is also used to study the an area of approximately 52,200 km [25]. /e study area lies interaction between groundwater and vegetation [10, 20]. within the northwestern marginal region of the East Asian For example, Engel et al. [21] observed diurnal GWL summer monsoon with a continental climate. /e summer ° ° fluctuations in wooded areas during the growing season, but and winter mean daily temperatures are 25.3 C and − 9.1 C, the neighboring grassland did not exhibit this phenomenon respectively [26]. /e mean annual precipitation is during the period. De Castro Ochoa and Reinoso [22] found ∼100 mm, which is mainly concentrated in May to Sep- that elevated temperatures caused an increase in the vege- tember and exhibits large interannual variability [27]. /e tation transpiration rate. When rising temperatures reach a mega-dunes and lakes in the desert are interdependent, and critical point, transpiration ceased due to leaf stomatal there are 110 perennial groundwater-recharged lakes, most closure, and these changes were reflected in daily GWL with an area <1 km [28]. /e groundwater recharge process fluctuations. Another study revealed that vegetation types, mainly occurs through cretaceous and tertiary sandstone, meteorological conditions, and soil properties jointly de- and the space that allows for shallow groundwater circu- termine the magnitude of diurnal GWL fluctuations [7]. lation is dominated by the quaternary gravel, fine sand, and Overall, the existing research highlights the close relation- fine silty sand [29]. ship between phreatophytes and their surrounding envi- /e Badain Jaran Desert has closed freshwater lakes, salt- ronment, but the actual field observation studies remain water lakes, and salt/brine lakes classified by the total dis- limited [23, 24]. /erefore, the dynamic characteristics of solved solids content in the water. /e vegetation landscape groundwater and its relationship with external conditions of the lake basin is characterized by ring zone distributions require further study. Moreover, the analysis of major around the water [30]. Waterfront regions are swampy factors affecting the ET rate would help elucidate the meadows, with a groundwater depth <0.5 m, and short and complex relationship between groundwater and vegetation. dense vegetation including species such as Triglochin mar- /e formation mechanism of the 110 permanent lakes at itima and Glaux maritima. /e second belt around the water the hinterland of the Badain Jaran Desert in northwestern is mostly saline meadow, with a groundwater depth of ∼1 m China is controversial, and the uncertainty of water con- and vegetation featuring Achnatherum splendens, Phrag- sumption in the lake basin is the primary cause of this mites australis, and Glycyrrhiza uralensis. /e outer belt has dispute. /e lake basins in the desert hinterland have a a groundwater depth of ∼2 m and vegetation cover com- considerable area with shallow groundwater, where vege- prising Nitraria tangutorum and Artemisia salsoloides. /e tation flourishes during approximately half of the year. outermost edge of vegetation is distributed among fixed and /erefore, ET cannot be neglected and is a critical com- semifixed sand dunes, connected to quicksand. /e lake ponent of the water balance calculation in the region. ecosystem in the Badain Jaran Desert, with minimal human However, the desert hinterland presents an adverse envi- activities, is ideal for the study of ecohydrological processes ronment for field workers, hindering the long-term research. in hyper-arid areas. /e research team has established ten Most monitoring methods can only be performed at a single GWL monitoring sites in the desert lake basins since 2010 site for in situ observations or in a small-scale area, which (Figure 1); the present study represents the first analysis of hinders the effective monitoring of the vegetation-driven the data from these sites. Hei River Advances in Meteorology 3 60°E 100°E 140°E 100°E 101°E 102°E 103°E 104°E 40°N Russia 42°N Kazakhstan Kyrgyzstan Mongolia Tajikistan N. Korea 30°N S. Korea Nepal Pakistan 41°N Bhutan 20°N P. R. China India Badain Jaran Desert Burma South 10°N Laos 40°N China Sea Vietnam 0° 0 800 1,600 3,200 km 39°N 035 70 140km G7 G8 40°0′N G2 G5 G4 39°50′N G10 G8 G3 G6 39°40′N G9 G1 0 5 10 20 km 101°30′E 101°45′E 102°0′E 102°15′E 102°30′E Legend Soil sample Monitoring well Weather station Figure 1: Location of the study area and observation sites. 2.2. Observation and Data Processing of GWL and Meteoro- total amount of hourly data were collected; otherwise, the logical Parameters. Ten groundwater observation wells were amplitude for that day was excluded from the analysis. established in different lake basins with shallow ground- Before groundwater series were applied to the White water, which were constructed with PVC screens with a method, a median smoothing filter in MATLAB was applied diameter of 8 cm. Hourly GWL was measured with a to eliminate noise. pressure transducer (Solinst 3001; Solinst Canada Ltd., To obtain meteorological parameters, a weather station Georgetown, ON, Canada), which had a measurement ac- (MAWS-301; Vaisala, Vantaa, Finland) was established on curacy of 0.1 cm, clock accuracy of ±1 min/year, and work the flat ground between Sumujilin South and North lakes at ° ° temperature range of − 10 C to 40 C. /e DTWT was 0∼2 m the hinterland of the Badain Jaran Desert. A QMH102 at the observation wells, and the transducers were fixed at sensor (Vaisala) was used to observe temperature ( C) and ∼30 cm below the water table. /e transducer measured both relative humidity (RH, %) with an observation interval of the total pressure of the water column above the probe and 10 s and an output interval of 10 min. An NR01 net radiation the atmospheric pressure, which was revised using a Bar- sensor (Hukseflux /ermal Sensors, Delft, the Netherlands) ologger barometer (Solinst). /e daily changes in GWL in was used to observe radiation (Rg, W/m), with output every the observation wells were calculated during the growing 30 min. Precipitation (mm) was monitored with a HOBO season, which was defined as the difference between the RG3-M (Onset Computer Corp., Bourne, MA, USA) tilting maximum and minimum values within a day (amplitude in rain gauge, recorded in rain events with an accuracy of cm). Because missing data could affect the calculation, data 0.2 mm/gauge. Based on the observed data, the daily mean were only considered if more than 90% of the normal daily temperature, daily mean RH, daily Rg, and daily Shiyang River 4 Advances in Meteorology precipitation were used for subsequent analysis. Rg and dWT (6) ET (t) � r(t) − S × . G y precipitation were the total amounts in a day, and the dt temperature was the mean of values at 03 : 00, 09 : 00, 15 : 00, /e estimated uncertainty of S (specific yield) is the and 21 : 00 (China Standard Time). Moreover, daily maxi- y major factor that causes ET estimation errors [23, 34]. /e mum and minimum temperature, RH, Rg, and wind speed G simulation experiments conducted by Loheide et al. [35] to (WS, m/s) were used to calculate potential evapotranspi- estimate S demonstrated that diurnal GWL fluctuations and ration (PET) based on the Penman–Monteith (FAO56) y antecedent moisture conditions had almost no influence on method [31]. /e data involved in this study were all ob- S . Furthermore, Meyboom [36] suggested that the readily servation records for the year 2014. y available S value should be half of the standard definition for S , whereas Loheide et al. [35] thought the suggestion 2.3. Estimation of ET from the Diurnal Fluctuations in should be based on the specific situation. To obtain the S GWL. In the revised White method, Loheide improved the value of the study area, soil samples at two depth ranges calculation accuracy in areas with shallow DTWT and ET (0–0.8 m and 0.8–1.5 m below the ground) were collected. can be estimated on an hourly basis [32]. /us, this im- /e soil moisture curve of the soil samples was determined proved method will be henceforth referred to as the Loheide using a pressure plate extractor (Daiki-3404; Daiki Rika method, which was applied to estimate the ET of eight Kogyo Co., Ltd, Saitama, Japan) to obtain important pa- observation sites under various vegetation conditions. rameters used in the van Genuchten model, and the Several assumptions were analyzed and discussed to explain properties of the soil samples were analyzed using a Mas- the suitability and reduce the uncertainty of the approach. tersizer 2000 laser diffractometer (Malvern Panalytical, Changes in groundwater storage near the observation wells Malvern, UK). /e gravel (>2 mm), sand (0.0625–2 mm), silt can be represented by the changes in GWL with time (0.004–0.0625 mm), and clay (<0.004 mm) contents of the (dWT/dt). /e changes in storage are controlled by the net 0–0.8 m soil sample were 0, 98.63, 1.37, and 0% and those of inflow or outflow of nearby groundwater (r(t)[L/T]) and the 0.8–1.5 m sample were 0, 78.58, 19.10, and 2.32%, re- ET : spectively. /e van Genuchten parameters θ (unitless), θ r s − 1 (unitless), α (cm ), and n (unitless) for the 0–0.8 m sample dWT (1) S � r(t) − ET (t), y G were 0.0291, 0.3873, 0.0425, and 2.363 and those for the dt 0.8–1.5 m sample were 0.0153, 0.3732, 0.0259, and 1.753, where S is the specific yield. respectively. /e S estimation method proposed by Crosbie When ET is zero, equation (1) can be simplified as et al. [37] was used: follows: yu dWT S � S − , y yu 1− (1/n) (2) S � r(t). 1 + α z + z /2 􏽨 􏼐 􏼐􏼐 􏼑 􏼑 􏼑􏽩 dt i f (7) /e recharge rate is a function of time [33]. Loheide [32] S � θ − θ , yu s r assumed that the head of the recharge source was constant. /us, the recharge rate of an observation well can be ob- where θ is the soil saturated moisture content, θ is the s r tained from the observed water table records, as expressed in residual moisture content, z and z are the initial and final i f equation (2): DTWT, and α and n are parameters in the van Genuchten model. dWT (3) r(WT) � S . dt 3.Results and Discussion /e method assumes that the head of the recovery re- charge has a similar change trend as the observed water table 3.1. Relationship between Diurnal GWL Fluctuations and record; therefore, the trend included in the GWL can be Vegetation. From the observed water table records, regular removed as follows: diurnal GWL fluctuations were detected at eight ground- WT (t) � WT(t) − m × t − b , (4) water observation sites, except wells G1 and G2, which were DT T T almost entirely comprised of bare sand (Table 1). /e ob- where WT (t) is the detrended GWL, WT(t) is the ob- DT servation sites where the fluctuations were detected were served GWL, m is the trendline slope, and b is the T T covered with various types of vegetation (see Table 1 for trendline intercept. main vegetation types). /is phenomenon emerged from Γ(WT ) is a function of dWT /dt and WT (t), and DT DT DT May to October, which was consistent with the growing a best-fit estimate of the function can be obtained based on season of the desert lakeside vegetation at the observation the detrended GWL from 00 : 00 to 06 : 00 of the day of sites (see Figure 2(d) for wells G6 and G8). /e findings interest and the following day. /en, to obtain the recharge mentioned above indicated that the diurnal GWL fluctua- rate function, tions were related to the vegetation covering lakeside areas. On a daily scale, the diurnal GWL fluctuations exhibited r(t) � S × 􏼂Γ(WT(t)) + m 􏼃. (5) y T a characteristics pattern whereby the water table decreased Finally, ET is calculated as continuously during the daytime and rose gradually at night, G Advances in Meteorology 5 Table 1: Classification of observation sites based on diurnal groundwater level fluctuations and the main vegetation profiles near the observation sites. No diurnal Diurnal fluctuation detected fluctuation Well no. G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 Main vegetation type Bare sand PA PA PA AS AS NT and AS NT and AS NT and AS PA: Phragmites australis; AS: Achnatherum splendens; NT: Nitraria tangutorum. 900 900 890 890 10 880 880 7 870 870 –10 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 Date Date Atmospheric pressure Temperature Precipitation (a) (b) 0.00 0.70 0.24 1.17 0.45 0.36 0.84 1.26 0.90 0.48 0.98 1.35 1.35 0.60 1.12 1.44 0.72 1.80 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 14/02/01 14/04/01 14/06/01 14/08/01 14/10/01 14/12/01 Date Date G1 G6 G2 G8 (c) (d) Figure 2: Daily temperature, precipitation, atmospheric pressure, and hourly groundwater level (GWL) fluctuations at observation sites G1, G2, G6, and G8 in 2014: (a) temperature and precipitation; (b) atmospheric pressure; (c) GWL fluctuations at observation sites G1 and G2, where no regular diurnal fluctuations were detected; (d) GWL fluctuations at observation sites G6 and G8, where regular diurnal fluctuations were detected. /e insets in (c) and (d) are derived from the corresponding groundwater level from June 5 to June 8. reaching a maximum in the morning and a minimum in the that in June, showing a larger amplitude of diurnal GWL afternoon. /is diel cycle is generally thought to be induced fluctuations in July. /e meteorological conditions in July by regular daily water consumption of phreatophytes, which that drove higher vegetation transpiration (daily average ° ° essentially represents the dynamic balance between the maximum and minimum temperature: 33.5 C and 17.3 C) groundwater lateral recharge and the consumption of could account for mentioned observations. At the end of groundwater by vegetation [6, 38–40]. On a seasonal scale, November, the vegetation entered the dormant period and there were variations in diurnal GWL fluctuations at the the diurnal GWL fluctuations decreased because of the lower eight sites, which was attributed to the interaction of veg- temperature in the desert (daily average maximum and ° ° etation with the surrounding environments [1, 7, 41]. To minimum temperature: 8.9 C and − 6.7 C). further understand the relationship between the diurnal /e diurnal GWL fluctuations exhibited variations GWL fluctuations and the vegetation at the observation sites among the observation sites. As illustrated in Figure 3, the on a seasonal scale, the diurnal GWL fluctuations during amplitudes of diurnal GWL fluctuations at observation site rainless periods at observation sites G4, G6, and G9 in G4 from June to September were larger than those of ob- different months are illustrated in Figure 3. In mid-April, the servation sites G6 and G9. To have a full understanding of diurnal GWL fluctuations of the three observation sites were these variations, the amplitudes of diurnal GWL fluctuations not obvious due to the low temperature (daily average of each observation site in the vegetation growing season and ° ° maximum and minimum temperature: 20.0 C and 3.9 C) nongrowing season were plotted as separate boxplots and no germinative vegetation. Figure 3 illustrates evident (Figure 4). Observation sites G3–G10 (with vegetation diurnal GWL fluctuations from June to September when cover) had larger amplitudes of diurnal GWL fluctuations in desert vegetation was in the growth stage and large amounts the growing season, but the amplitudes in the nongrowing of groundwater were consumed. /e diurnal GWL fluctu- season were negligible. At sites G1 and G2 (the bare sand ations in July were more obvious at observation sites G6 and sites), no evident diurnal GWL fluctuations were observed in G9, although the DTWT in July was higher compared with either the vegetation growing season or the nongrowing G1 depth to Temperature (°C) water table (m) Precipitation G2 depth to (mm/d) water table (m) G6 depth to water table (m) Atmospheric pressure (cmH O) G8 depth to water table (m) 6 Advances in Meteorology 0.72 0.90 14/4/16–4/20 14/4/16–4/20 0.95 14/11/20–11/24 0.90 14/11/20–11/24 1.00 1.08 14/9/15–9/19 14/6/6–6/10 14/7/7–7/11 1.05 14/6/6–6/10 1.26 1.10 14/7/7–7/11 14/9/15–9/19 1.44 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 0:00 Hours Hours (a) (b) 1.35 1.40 14/4/16–4/20 1.45 14/11/20–11/24 1.50 14/6/6–6/10 1.55 14/7/7–7/11 1.60 1.65 14/9/15–9/19 0:00 0:00 0:00 0:00 0:00 0:00 Hours (c) Figure 3: Seasonal variations in diurnal groundwater level fluctuations at observation site (a) G4, (b) G6, (c) G9. 12 12 10 10 Diurnal fluctuation was detected 8 8 Diurnal fluctuation was not detected 6 6 2 2 0 0 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 25%–75% 25%–75% Min-Max Min-Max Median Median (a) (b) Figure 4: Comparison of boxplots of the amplitude in groundwater level (GWL) during the (a) growing season and (b) nongrowing season. /e vertical dashed line in (a) separates sites with no regular diurnal GWL fluctuations (G1 and G2) and those with fluctuations (G3–G10); no fluctuations were detected in (b). season. Furthermore, the amplitudes of diurnal GWL by Phragmites australis, were larger than those of the sites fluctuations in the growing season varied among the sites G6–G10, with Achnatherum splendens and Nitraria tangu- G3–G10, which was attributed to the vegetation type. For torum. Interestingly, site G3, also mainly covered with instance, the amplitudes at sites G4 and G5, mainly covered Phragmites australis, had a smaller amplitude of diurnal Daily change of GWL (cm) Depth to water table (m) Depth to water table (m) Daily change of GWL (cm) Depth to water table (m) Advances in Meteorology 7 GWL fluctuations than sites G4 and G5, indicating that rates of observation sites G4 and G7 were markedly higher from vegetation type was not the only factor determining the mid-July to early September than other growth stages. /is variations in diurnal GWL fluctuations. /us, climatic indicated that the optimal growth periods varied among sites conditions, soil properties, and groundwater dynamic with different vegetation types and vitality. Due to the better processes might also cause differences in water consumption vegetation coverage and growth condition of sites G4 and G7, and recharge balance, manifesting as a spatial difference in the vegetation maintained relatively good growth and con- diurnal GWL fluctuations. sumed large amounts of groundwater even when the vegetation in other sites entered their final growth stage in September. To explain the discrepancy, a previous study suggested that dif- 3.2. Estimation of ET under 3ree Vegetation Covers. ferences in the types of deciduous and nondeciduous vegetation /e White method was used to estimate the ET rate of led to different periods reaching a maximum ET [43]. /e observation sites with three vegetation covers. /e GWL vegetation in the present study comprised herbaceous and small records of observation sites G3–G10 during the vegetation shrub plants, with different vegetation coverages from those of growing season were selected to analyze the ET rate, and the study mentioned above. /us, it is logical that the un- the statistical results are summarized in Table 2. /e ET synchronized maximum daily mean ET was driven mainly by rate was associated with vegetation type, especially in the vegetation type and growth vitality. middle of the growing season. From the relationship be- tween vegetation type and the daily average ET rate, the observation sites could be grouped into three categories: the 3.3. Controlling Factors of ET Rate. Vegetation type, me- ET rate at sites G4 and G5, with well-grown Phragmites teorological parameters, and groundwater dynamics jointly australis coverage, was ∼5 mm/d; the daily average ET of led to spatiotemporal variations in the diurnal GWL fluc- observation sites G6 and G7 dominated by Achnatherum tuations at the observation sites, which ultimately was re- splendens coverage was 3∼4 mm, including site G7 with the flected in the ET . To further explore the key factors affecting best vegetation coverage in this category, where the ET rate the ET rate at the observation sites and the mechanisms that G G reached 4 mm/d; the remaining three observation sites control ET in the Badain Jaran Desert, the relationship G8–G10 were mainly covered by Nitraria tangutorum, and between ET rate with DTWTand meteorological parameters the daily average ET rate was relatively low. Overall, the was analyzed. Taking observation site G6 as an example order of groundwater consumption by the three vegetation (Figure 6), there was a significant positive correlation between covers in this study was Phragmites australis > Achnatherum ET rate and air temperature, which was consistent with the splendens > Nitraria tangutorum. /ese results were con- result that temperature was the key factor controlling the sistent with the ET rate of similar research areas. For in- seasonal-scale variations in the diurnal GWL fluctuations stance, an area covered with Salix psammophila in the Mu Us obtained from Figure 3. Moreover, the ET rate at site G6 was Desert, China, had a DTWTof 1∼1.5 m and daily ET rate of positively correlated with solar radiation but had no signif- 3∼4 mm in the growing season [42], which was similar to the icant relationship with wind speed, relative humidity, and estimates for the observation sites with Nitraria tangutorum other factors. /ese results indicated that air temperature and and Achnatherum splendens in this study. /is similarity solar radiation are the key meteorological factors controlling supports the accuracy of the estimated results in repre- the ET rate at this observation site. senting the ET rate of desert lakeside vegetation. /e relationship between the ET rate and the external G G In this study, ET rates were also related to vegetation factors at site G6 indicated that the meteorological condi- vitality. For example, site G3 with Phragmites australis, tions in the Badain Jaran Desert were among the most compared with sites G4 and G5 with similar vegetation, had critical factors controlling the ET rate. To verify this poorer vegetation conditions with sparse patches and weak conclusion, the relationship between ET rate and PET at viability. As mentioned above, the amplitudes of diurnal GWL eight observation sites (i.e., as a comprehensive measure of fluctuations at this site were narrower, which were ultimately meteorological condition) were investigated. Significant reflected in a lower ET rate than that at sites G4 and G5. positive correlations (R � 0.38∼0.54) were observed at all Other sites also showed a similar situation, which was reflected sites except G7 (Figure 7), which demonstrated the influence in the maximum daily ET rate. Observation site G3 only had of meteorological factors on ET rate. For comparison, a G G a maximum daily ET rate of 6.2mm/d, which was similar to previous study in a semi-arid area suggested that ET was G G that of observation sites with Nitraria tangutorum. In contrast, weakly positively correlated with PET (R � 0.02∼0.32) [19], the maximum daily ET rate at site G6, with Achnatherum and Lautz [23] also reported a similar correlation between splendens, reached 11.3 mm/d, which was slightly lower than PET and ET in a riparian region. In contrast, the ET G G that of the sites with well-grown Phragmites australis. observed by Yuan et al. [18] showed a significant positive On a seasonal scale, ET is controlled by vegetation correlation with PET in desert riparian forests. Mazur et al. phenology [7]. In this study, the estimated ET was higher [17] also recognized a positive correlation and estimated the from July to August than in other stages of the growing ET (i.e., interpolated ET rate) based on the significant season (Figure 5). Moreover, the period during which the relationship between the estimated ET and PET when the peak daily mean ET occurred was not perfectly consistent White method was not appropriate for ET estimation. G G among the observation sites. /e highest daily mean ET at /erefore, the correlation between the estimated ET and G G G3, G6, G9, and G10 occurred in early July, whereas the ET PET differed among the observation sites and the G 8 Advances in Meteorology Table 2: Statistics of the estimated groundwater evapotranspiration (ET ) of observation sites during the growing season in 2014, number of days used in the estimation, and the specific yield (S ). ET (mm) Well no. Number of days S . Total Daily mean Daily max. Daily min. SD G3 169 568.7 3.4 6.2 0.3 1.5 0.18 G4 158 784.9 5.0 11.0 0.1 2.6 0.21 G5 149 757.4 5.1 13.2 0.3 3.2 0.22 G6 160 566.4 3.5 11.3 0.3 1.8 0.20 G7 160 667.2 4.0 8.3 0.6 1.7 0.23 G8 182 538.0 2.9 6.0 0.2 1.3 0.22 G9 154 590.1 3.8 7.9 0.1 2.1 0.23 G10 165 494.7 3.0 5.3 0.1 1.1 0.19 2014/5/1 2014/7/1 2014/9/1 2014/11/1 Date G3 G4 (a) 2014/5/1 2014/7/1 2014/9/1 2014/11/1 Date G6 G7 (b) 2014/5/1 2014/7/1 2014/9/1 2014/11/1 Date G9 G10 (c) Figure 5: Estimated daily groundwater evapotranspiration (ET ) of observation sites: (a) G3 and G4, (b) G6 and G7, and (c) G9 and G10 with different vegetation covers. ET (mm/d) ET (mm/d) ET (mm/d) G G G Advances in Meteorology 9 8 8 y = 26.18x – 24.00 y = 0.99x – 0.85 2 2 R = 0.49 R = 0.54 6 6 4 4 2 2 0 0 0.95 1.00 1.05 1.10 1.15 128 37 46 5 DTWT (m) PET (mm/d) (a) (b) 8 8 y = 0.26x – 2.10 y = 0.01x – 0.70 2 2 R = 0.77 R = 0.28 6 6 4 4 2 2 0 0 5 10 15 20 25 30 35 120 180 240 300 360 Air temperature (°C) Solar radiation (W/m ) (c) (d) 8 8 y = –0.09x – 4.67 y = –0.02x + 4.27 2 2 R = 0.03 R = 0.02 6 6 4 4 2 2 0 0 0 510 15 20 0 10 20 30 40 50 60 70 Wind speed (m/s) Relative humidity (%) (e) (f) Figure 6: Relationship of diurnal groundwater level fluctuations with depth to water table (DTWT), potential evapotranspiration (PET), air temperature, solar radiation, wind speed, and relative humidity at observation site G6. relationship between the two should be explored before level decline resulted in a 62% reduction in ET , high- applying the estimation method by Mazur et al. [17]. lighting the impact DTWTchanges on ET . In this study, the In this study, estimated ET and PET showed relatively DTWT range varied among the observation sites, and their high correlations at most observation sites, and there were relationships with vegetation type, vegetation vitality, and few precipitation events. /erefore, the White and Mazur’s evapotranspiration rate were consistent with the trends estimation methods could be jointly used to quantify the observed by Jackson et al. [44]. For instance, observation annual groundwater consumption (see Figure 8 for the sites G4 and G5, which were mainly covered densely with estimated results at observation site G6). Observation sites Phragmites australis, had a DTWTof 0.7∼1.5 m. Meanwhile, G3, G5, G6, G8, G9, and G10 were selected for this appli- sites G8 and G9 covered sparsely with Nitraria tangutorum, cation based on the significant relationships between the had a DTWTof 1.2∼1.7 m. Correspondingly, under the same estimated ET and PET, where the total ET in the whole meteorological conditions, the ET rate of the sites with G G G growing season (total 184 days) was obtained. /e ET Phragmites australis was markedly higher than that of sites ranged from 600 to 900 mm at observation sites with with Nitraria tangutorum. Phragmites australis and varied depending on the coverage /e estimated total ET at each observation site in the conditions, and it ranged from approximately 600 to growing season was greater than the total annual precipi- 650 mm at the sites covered with Achnatherum splendens. At tation in that year (103.7 mm), but there was no difference in sites with Nitraria tangutorum and Achnatherum splendens, the DTWT at the beginning and end of the growing season. the ET ranged from 500 to 650 mm. Overall, the water level first declined and then returned to In general, the amount of groundwater available for the original level (Figure 9), indicating that groundwater was vegetation roots decreases as DTWT increases, resulting in a the primary recharge source of the lakes in this study area, negative relationship between DTWT and ET rate [44]. with only a small amount of precipitation recharge. Fur- Furthermore, Cooper et al. [45] found that a 1.6 m water thermore, the total amount of lateral recharge and ET (mm/d) ET (mm/d) G ET (mm/d) G G ET (mm/d) ET (mm/d) ET (mm/d) G G 10 Advances in Meteorology 8 12 y = 0.77x + 0.06 y = 0.89x + 1.1 R = 0.45 R = 0.19 4 6 0 0 128 37 46 5 128 37 46 5 PET (mm/d) PET (mm/d) (a) (b) 15 8 y = 1.64x – 2.1 y = 0.99x – 0.85 12 R = 0.43 R = 0.54 0 0 1428 3 5 6 7 1628 37 4 5 PET (mm/d) PET (mm/d) (c) (d) y = 0.37x + 2.35 y = 0.58x + 0.50 8 R = 0.08 2 R = 0.38 0 0 1 2345678 128 3 4567 PET (mm/d) PET (mm/d) (e) (f) y = 0.59x + 0.45 8 y = 1.12x – 1.13 R = 0.50 R = 0.48 6 4 0 0 128 3 4567 128 3 4567 PET (mm/d) PET (mm/d) (g) (h) Figure 7: Relationship between the estimated daily groundwater evapotranspiration (ET ) and potential evapotranspiration (PET) at the eight observation sites with vegetation coverage. (a) G3, Phragmites australis (DTWT: 0.74–1.05 m). (b) G4, Phragmites australis (DTWT: 0.83–1.45 m). (c) G5, Phragmites australis (DTWT: 0.75–1.45 m). (d) G6, Achnatherum splendens (DTWT: 0.97–1.15 m). (e) G7, Ach- natherum splendens (DTWT:1.19–1.55 m). (f) G8, Nitraria tangutorum and Achnatherum splendens (DTWT:1.27–1.39 m). (g) G9, Nitraria tangutorum and Achnatherum splendens (DTWT: 1.43–1.69 m). (h) G10, Nitraria tangutorum and Achnatherum splendens (DTWT: 0.86–1.05 m). precipitation recharge of groundwater was greater than the observation site G6 (Figure 6), and the water table decreased total ET . /erefore, groundwater is the main water source by ∼0.2 m from May to August, while the estimated ET G G for vegetation growth in this case, and the ET process is showed an increasing trend (Figure 9). /is seemingly dependent on groundwater recharge. However, a positive contradictory phenomenon can be explained by the fact that correlation was indicated between ET rate and DTWT at the ET rate was jointly controlled by the DTWT and PET, G G ET (mm/d) ET (mm/d) ET (mm/d) ET (mm/d) G G G G ET (mm/d) ET (mm/d) ET (mm/d) ET (mm/d) G G G G Advances in Meteorology 11 10 recharge rates change on a daily basis and there are errors in recharge rates using the constant rise rates of GWL at night; 6 accurate specific yields are not easy to attain; with the ad- vancement of high-frequency digital data collection devices, recent studies have found that transpiration of some veg- etation did not stop completely at night [5, 46]. To overcome these deficiencies, many studies proposed several revised methods. In regard to the calculation of groundwater re- charge rates, Troxell [33] questioned whether recharge rates 0 0 were constant on a daily basis. Later, scholars represented by 14/05/01 14/06/01 14/07/01 14/08/01 14/09/01 14/10/01 14/11/01 Gribovszki et al. [14] and Loheide [32] proposed new Date methods to obtain the dynamic daily recharge rates, which PET improved the ET estimation accuracy. ET Considering the basic assumptions and their limitations, ET there are some observations to be explained in the appli- Figure 8: Estimated groundwater evapotranspiration (ET ) and cation of the groundwater diurnal signal method in Badain interpolated evapotranspiration rate (ET ) based jointly on the Jaran Desert. Due to their diverse mechanisms, GWL di- white and Mazur’s methods and potential evapotranspiration urnal fluctuations can be categorized into various types [40]. (PET) at the observation site G6. /e GWL series are easily distinguished from each other except the cycles induced by atmospheric pressure and evapotranspiration, as the two have similar fluctuant shapes. 0.72 10 R = 0.49 Atmospheric pressure effects can also occur in unconfined 0.80 aquifers typically mirroring each other [47–49]. /e diurnal 0.88 cycles induced by atmospheric pressure and evapotranspi- ration are both continuous time series. However, there are 0.96 significant differences in their daily variations. /e pressure- 1.04 dominated GWL cycles are closely related to the fluctuations 1.12 of atmospheric pressure in each season, and the valley and –2 peak values of GWL correspond to the maximum and 1.20 –4 minimum air pressure throughout the day, respectively. 14/05/01 14/06/01 14/07/01 14/08/01 14/09/01 14/10/01 14/11/01 Similar to the GWL diurnal fluctuations observed by Jiang Date et al. [50], air pressure reached a maximum at approximately ET 10 : 00 am each day when the water level was the lowest. DTWT Conversely, the pressure was lowest at 3 : 00 pm when GWL Figure 9: Estimated daily groundwater evapotranspiration (ET ) reached its maximum. In the case of the Badain Jaran Desert, of observation site G6 and the depth to water table (DTWT) in the diurnal GWL fluctuations are completely different from the growing season in 2014. above type and are consistent with the ET-diurnal cycles [40]. /e second assumption in the White method (i.e., the and the effect of PETexceeded that of DTWT in that period. transpiration of vegetation at night is negligible) can be During the observed period in this study, changes in DTWT had no marked influence on the ET rate because DTWTs confirmed by comparing the results in this study with other research results in nearby desert areas. For example, Yuan were shallow (i.e., all within 2 m) and presented relatively small changes, all within 2 m. /erefore, observation sites et al. [18] thought that groundwater evapotranspiration at night was weak based on observations using the eddy co- with a wider DTWTrange can be set up in future research to further discuss the influence of DTWT on the ET rate. variance method in areas covered by desert vegetation. /e site chosen for observation was dominated by Tamarix, where Phragmites australis, Glycyrrhiza inflata, and other 3.4. Limitations and Observations on Applying the Proposed types also grow, which was rather similar to the observation sites in this study. Additionally, other studies based on the Method. /e application of the White method and its re- vised methods are based on four basic assumptions: (1) White method estimated groundwater evapotranspiration in regions of Mu Us and Gobi Deserts that were covered with reduction in GWL is induced only by vegetation; (2) the transpiration at night is weak and negligible; (3) the average vegetation [11, 42, 43]. Although vegetation transpiration at raise rates of GWL at night can represent recharge rates of night could be negligible in most cases, other studies have groundwater in a given day; (4) the specific yields are determined that transpiration at night is considerable in representative and reliable. Although the groundwater signal certain environments. /erefore, the ET estimation in methods have been widely verified and applied in studies on these cases is based on the reliability of the assumption. In vegetation evapotranspiration of arid regions, the assump- future research, it is necessary to monitor the vegetation tions still have certain limitations. /ese shortages are water consumption at night to reduce the ET estimation specifically reflected in the following scenarios: lateral GWL uncertainty. DTWT (m) PET (mm/d) ET (mm/d) ET and ET (mm/d) G G C 12 Advances in Meteorology Methods to obtain aquifer specific yields are also im- characteristics of the regular diurnal GWL fluctuations in portant in research involving the White method. Specific spatiotemporal scales and their relationship with vegeta- yields are not only related to the aquifer soil texture but also tion were analyzed. Afterwards, the ET was estimated via affected by the depth to water table, dynamic rates of the White method, and the main controlling factors of ET temporal change of GWL, and drainage time. /ere are were explored. /e observed series indicated that diurnal various methods based on factual research conditions GWL fluctuations were consistent with the vegetation aiming to obtain a representative and reliable specific yield. growing season, which was driven by groundwater con- Gribovszki [51] recently proposed a diurnal method to sumption by phreatophytes. /e amplitudes of diurnal obtain dynamic specific yield and compared it with tradi- GWL fluctuations showed marked differences among the tional specific yield estimation techniques. Moreover, sites, which was attributed to vegetation type and vitality. pumping test estimations are considered to be a relatively On a seasonal scale, the air temperature was an important low-cost and suitable method, as it is the most similar to the external factor affecting the diurnal GWL fluctuations by dynamic specific yield. While it would be difficult to im- controlling the vegetation phenology and groundwater plement a slug test at the hinterland studied herein, relatively consumption. reliable specific yield could be feasibly obtained via a pF /e estimated ET was associated with vegetation type curve (a texture-based field capacity value). However, the and exhibited a degree of association with the vegetation specific yields obtained by this method are nondynamic and growth stage. /e observation sites could be grouped into may also become a source of ET estimation errors. three categories based on the estimated results and vege- /erefore, observations on the specific yield estimation in tation type, and the ET followed the general order this study are further explained below. Phragmites australis > Achnatherum splendens > Nitraria Specific yields strongly depend on the DTWT in shallow tangutorum. In the lake basins of the Badain Jaran Desert, groundwater environments and are dynamic in reality. the vegetation grew in the area where the DTWT was within Existing research [5] combined drainage experiments and ∼2 m, and meteorological factors had a greater influence on simulation modeling methods to obtain the changes in vegetation transpiration than in other areas with higher specific yields with DTWT within 0∼2 m. When DTWT was DTWTs. Moreover, DTWT and PET are both important within 0.8 m, specific yields varied as DTWTs became factors controlling the ET rate, while their influence varied deeper. /en, there was a difference when depth is greater among observation sites. than 0.8 m, and the effect of DTWTchange on specific yields /is study further expands the geographical applicability began decrease and approached a constant value that of the White method and offers a new research paradigm for depended on soil texture. In another case, observations the study of diurnal GWL fluctuations in groundwater- demonstrated that there was no significant correlation be- dependent lakeside ecosystems. Additionally, the study re- tween specific yields and DTWT [51], for which the range of sults could become a scientific basis to solve the controversy GWL changes was thought to be small and depth com- on the formation mechanism of the lakes in the Badain Jaran pensation in groundwater was not a key issue to consider. Desert and support the sustainable usage of regional DTWTs varied from 0.8 to 1.1 m in the second case; groundwater resources. therefore, to some extent, the results of the two studies mentioned above were consistent. In the present study of the Data Availability Badain Jaran Desert, the DTWTs of each observation site was greater than 0.8 m (Figure 7) except the observation sites /e groundwater level data and meteorological data used to G3 and G5, which had DTWTs of less than 0.8 m during a support the findings of this study were supplied by the short period of the beginning and end in the growing season. Center for Glacier and Desert Research, Lanzhou University, From this perspective, the changes in DTWTs at most ob- under license and so cannot be made freely available. Re- servation sites had little effect on the specific yield estima- quests for access to these data should be made to the cor- tions. Nevertheless, the specific yield estimation in this study responding author. was nondynamic as it did not account for the dynamic GWL temporal change rates relative to a pumping test estimation Conflicts of Interest or the dynamic diurnal method. /erefore, the specific yield estimation may become a source of ET estimation errors. /e authors declare that they have no conflicts of interest. According to Gribovzxki’s comparison, the estimated spe- cific yield in this study will be slightly higher than that Acknowledgments calculated by the dynamic method. In future research, soil moisture monitoring instruments are expected to be added We gratefully acknowledge the funding from the National at groundwater observation sites to observe dynamic specific Natural Science Foundation of China (41530745 and yields and thus improve ET estimation accuracy. 41871021) and the Fundamental Research Funds for the Central University, Lanzhou University (lzujbky-2016-275). We thank Niu Zhenmin, Xu Xingbin, Liang Xiaoyan, and 4.Conclusions Wen Penghui from the Center for Glacier and Desert Re- Based on observed GWL and meteorological parameters in search, Lanzhou University, for their contribution to the the lake basins of the Badain Jaran Desert, the field work in this study. Advances in Meteorology 13 groundwater level fluctuations,” Journal of Hydrology, References vol. 349, no. 1-2, pp. 6–17, 2008. [1] F. Orellana, P. Verma, S. P. Loheide, and E. Daly, “Monitoring [15] P. Wang and S. P. Pozdniakov, “A statistical approach to and modeling water-vegetation interactions in groundwater- estimating evapotranspiration from diurnal groundwater dependent ecosystems,” Reviews of Geophysics, vol. 50, no. 3, level fluctuations,” Water Resources Research, vol. 50, no. 3, Article ID RG3003, 2012. pp. 2276–2292, 2014. [2] E. Naumburg, R. Mata-gonzalez, R. G. Hunter, T. Mclendon, [16] C. E. Hughes, J. D. Kalma, P. Binning, G. R. Willgoose, and and D. W. Martin, “Phreatophytic vegetation and ground- M. Vertzonis, “Estimating evapotranspiration for a temperate water fluctuations: a review of current research and appli- salt marsh, Newcastle, Australia,” Hydrological Processes, cation of ecosystem response modeling with an emphasis on vol. 15, no. 6, pp. 957–975, 2001. great basin vegetation,” Environmental Management, vol. 35, [17] M. L. C. Mazur, M. J. Wiley, and D. A. Wilcox, “Estimating evapotranspiration and groundwater flow from water-table no. 6, pp. 26–740, 2005. [3] X. Li, L. Lu, G. Cheng, and H. Xiao, “Quantifying landscape fluctuations for a general wetland scenario,” Ecohydrology, vol. 7, no. 2, pp. 378–390, 2014. structure of the Heihe River Basin, north-west China using [18] G. Yuan, Y. Luo, M. Shao, P. Zhang, and X. Zhu, “Evapo- FRAGSTATS,” Journal of Arid Environments, vol. 48, no. 4, transpiration and its main controlling mechanism over the pp. 521–535, 2001. desert riparian forests in the lower Tarim River Basin,” Science [4] S. P. Loheide and S. M. Gorelick, “Riparian hydroecology: a China Earth Sciences, vol. 58, no. 6, pp. 1032–1042, 2015. coupled model of the observed interactions between [19] W. F. Yue, T. J. Wang, T. E. Franz, and X. H. Chen, “Spa- groundwater flow and meadow vegetation patterning,” Water tiotemporal patterns of water table fluctuations and evapo- Resources Research, vol. 43, no. 7, Article ID W07414, 2007. transpiration induced by riparian vegetation in a semiarid [5] J. Fan, K. T. Ostergaard, A. Guyot, S. Fujiwara, and area,” Water Resources Research, vol. 52, no. 3, pp. 1948–1960, D. A. Lockington, “Estimating groundwater evapotranspira- tion by a subtropical pine plantation using diurnal water table [20] P. Wang, Y. Zhang, J. Yu, G. Fu, and F. Ao, “Vegetation fluctuations: implications from night-time water use,” Journal dynamics induced by groundwater fluctuations in the lower of Hydrology, vol. 542, pp. 679–685, 2016. Heihe River Basin, northwestern China,” Journal of Plant [6] W. N. White, “A method of estimating ground-water supplies Ecology, vol. 4, no. 1-2, pp. 77–90, 2011. based on discharge by plants and evaporation from soil: re- [21] V. Engel, E. G. Jobbagy, ´ M. Stieglitz, M. Williams, and sults of investigations in Escalante Valley, Utah,” USGS R. B. Jackson, “Hydrological consequences of Eucalyptus Numbered Series, Water Supply Paper 659-A, U.S. Govern- afforestation in the Argentine Pampas,” Water Resources ment Printing Office, Washington, DC, USA, 1932. Research, vol. 41, no. 10, Article ID W10409, 2005. [7] J. J. Butler, G. J. Kluitenberg, D. O. Whittemore et al., “A field [22] F. De Castro Ochoa and J. M. Reinoso, “Model of long-term investigation of phreatophyte-induced fluctuations in the water-table dynamics at doñana national park,” Water Re- water table,” Water Resources Research, vol. 43, no. 2, Article search, vol. 31, no. 10, pp. 2586–2596, 1997. ID W02404, 2007. [23] L. K. Lautz, “Estimating groundwater evapotranspiration [8] J. Z. Drexler, R. L. Snyder, D. Spano, and K. T. Paw U, “A rates using diurnal water-table fluctuations in a semi-arid review of models and micrometeorological methods used to riparian zone,” Hydrogeology Journal, vol. 16, no. 3, estimate wetland evapotranspiration,” Hydrological Processes, pp. 483–497, 2008. vol. 18, no. 11, pp. 2071–2101, 2004. [24] J. Fan, K. T. Oestergaard, A. Guyot, and D. A. Lockington, [9] J. Zhu, M. Young, J. Healey, R. Jasoni, and J. Osterberg, “Estimating groundwater recharge and evapotranspiration “Interference of river level changes on riparian zone evapo- from water table fluctuations under three vegetation covers in transpiration estimates from diurnal groundwater level a coastal sandy aquifer of subtropical Australia,” Journal of fluctuations,” Journal of Hydrology, vol. 403, no. 3-4, Hydrology, vol. 519, no. Part A, pp. 1120–1129, 2014. pp. 381–389, 2011. [25] J. F. Zhu, N. A. Wang, H. B. Chen, C. Y. Dong, and [10] L. Yin, Y. Zhou, S. Ge, D. Wen, E. Zhang, and J. Dong, H. A. Zhang, “Study on the boundary and the area of Badain “Comparison and modification of methods for estimating Jaran Desert based on remote sensing imagery,” Progress in evapotranspiration using diurnal groundwater level fluctua- Geography, vol. 29, no. 9, pp. 1087–1094, 2010. tions in arid and semiarid regions,” Journal of Hydrology, [26] N. Ma, N. A. Wang, Z. L. Li, X. L. Chen, J. F. Zhu, and vol. 496, pp. 9–16, 2013. C. Y. Dong, “Analysis on climate change in the northern and [11] P. Wang, S. O. Grinevsky, S. P. Pozdniakov et al., “Application southern marginal zones of the Badain Jaran Desert during of the water table fluctuation method for estimating evapo- the period 1960–2009,” Arid Zone Research, vol. 28, no. 2, transpiration at two phreatophyte-dominated sites under pp. 242–250, 2011. hyper-arid environments,” Journal of Hydrology, vol. 519, [27] N. A. Wang, N. Ma, H. B. Chen, X. L. Chen, C. Y. Dong, and no. Part B, pp. 2289–2300, 2014. Z. Y. Zhang, “A preliminary study of precipitation charac- [12] I. C. Reigner, “A method of estimating streamflow loss by teristics in the hinterland of Badain Jaran desert,” Advances in evapotranspiration from the riparian zone,” Forest Science, Water Science, vol. 24, no. 2, pp. 153–160, 2013. vol. 12, no. 2, pp. 130–139, 1966. [28] N. Wang, K. Ning, Z. Li, Y. Wang, P. Jia, and L. Ma, “Ho- [13] P. Bauer, G. /abeng, F. Stauffer, and W. Kinzelbach, “Es- locene high lake-levels and pan-lake period on Badain Jaran timation of the evapotranspiration rate from diurnal Desert,” Science China Earth Sciences, vol. 59, no. 8, groundwater level fluctuations in the Okavango Delta, Bot- pp. 1633–1641, 2016. swana,” Journal of Hydrology, vol. 288, no. 3-4, pp. 344–355, [29] X. S. Wang, X. N. Hu, X. M. Jin, L. Z. Hou, R. Y. Qian, and 2004. L. D. Wang, “Interactions between groundwater and lakes in [14] Z. Gribovszki, P. Kalicz, J. Szilagyi, ´ and M. Kucsara, “Riparian Badain Jaran Desert,” Earth Science Frontiers, vol. 21, no. 4, zone evapotranspiration estimation from diurnal pp. 91–99, 2014. 14 Advances in Meteorology [30] Z. D. Zhu, Z. Wu, and S. Liu, An Introduction to Chinese [46] M. A. Caird, J. H. Richards, and L. A. Donovan, “Nighttime Deserts, Science Press, Beijing, China, 1980. stomatal conductance and transpiration in C3 and C4 plants,” [31] R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, “Crop Plant Physiology, vol. 143, no. 1, pp. 4–10, 2007. evapotranspiration: guidelines for computing crop water [47] P. W. Hare and R. E. Morse, “Water-level fluctuations due to barometric pressure changes in an isolated portion of an requirement,” FAO Irrigation and Drainage Paper No. 56, unconfined aquifer,” Ground Water, vol. 35, no. 4, pp. 667– pp. 1–15, Food and Agriculture Organization of the United 671, 1997. Nations, Rome, Italy, 1980. [48] X. Sun, Y. Xiang, Z. Shi, X. Hu, and H. Zhang, “Sensitivity of [32] S. P. Loheide, “A method for estimating subdaily evapo- the response of well-aquifer systems to different periodic transpiration of shallow groundwater using diurnal water loadings: a comparison of two wells in Huize, China,” Journal table fluctuations,” Ecohydrology, vol.1, no.1, pp. 59–66, 2008. of Hydrology, vol. 572, pp. 121–130, 2019. [33] H. C. Troxell, “/e diurnal fluctuation in the ground-water [49] H. Zhang, Z. Shi, G. Wang, X. Sun, R. Yan, and C. Liu, “Large and flow of the santa ana river and its meaning,” Transactions, earthquake reshapes the groundwater flow system: insight American Geophysical Union, vol.17, no. 2, pp. 496–504, 1936. from the water-level response to earth tides and atmospheric [34] R. W. Healy and P. G. Cook, “Using groundwater levels to pressure in a deep well,” Water Resources Research, vol. 55, estimate recharge,” Hydrogeology Journal, vol. 10, no. 1, no. 5, pp. 4207–4219, 2019. pp. 91–109, 2002. [50] X.-W. Jiang, Z.-C. Sun, K.-Y. Zhao et al., “A method for [35] S. P. Loheide, J. J. Butler, and S. M. Gorelick, “Estimation of simultaneous estimation of groundwater evapotranspiration groundwater consumption by phreatophytes using diurnal and inflow rates in the discharge area using seasonal water water table fluctuations: a saturated-unsaturated flow as- table fluctuations,” Journal of Hydrology, vol. 548, pp. 498– sessment,” Water Resources Research, vol. 41, no. 7, Article ID 507, 2017. W07030, 2005. [51] Z. Gribovszki, “Comparison of specific-yield estimates for [36] P. Meyboom, Bulletin 139-Groundwater Studies in the As- calculating evapotranspiration from diurnal groundwater- siniboine River Drainage Basin Part II: Hydrologic Charac- level fluctuations,” Hydrogeology Journal, vol. 26, no. 3, teristics of Phreatophytic Vegetation in South-Central pp. 869–880, 2018. Saskatchewan, Department of Energy, Mines and Resources Geological Survey of Canada, Canada, 1967. [37] R. S. Crosbie, P. Binning, and J. D. Kalma, “A time series approach to inferring groundwater recharge using the water table fluctuation method,” Water Resources Research, vol. 41, no. 1, Article ID W01008, 2005. [38] A. R. Croft, “Water loss by stream surface evaporation and transpiration by riparian vegetation,” Transactions, American Geophysical Union, vol. 29, no. 2, pp. 235–239, 1948. [39] H. M. Tschinkel, “Short-term fluctuation in streamflow as related to evaporation and transpiration,” Journal of Geo- physical Research, vol. 68, no. 24, pp. 6459–6469, 1963. [40] Z. Gribovszki, J. Szilagyi, ´ and P. Kalicz, “Diurnal fluctuations in shallow groundwater levels and streamflow rates and their interpretation—a review,” Journal of Hydrology, vol. 385, no. 1–4, pp. 371–383, 2010. [41] P. Csafordi, ´ A. Szabo, ´ K. Balog, Z. Gribovszki, A. Bidlo, ´ and T. To´th, “Factors controlling the daily change in groundwater level during the growing season on the Great Hungarian Plain: a statistical approach,” Environmental Earth Sciences, vol. 76, no. 20, p. 675, 2017. [42] D.-H. Cheng, Y. Li, X. Chen, W.-K. Wang, G.-C. Hou, and C.-L. Wang, “Estimation of groundwater evaportranspiration using diurnal water table fluctuations in the Mu Us Desert, northern China,” Journal of Hydrology, vol. 490, pp. 106–113, [43] D. Cheng, J. Duan, K. Qian, L. Qi, H. Yang, and X. Chen, “Groundwater evapotranspiration under psammophilous vegetation covers in the Mu Us Sandy Land, northern China,” Journal of Arid Land, vol. 9, no. 1, pp. 98–108, 2017. [44] R. B. Jackson, J. Canadell, J. R. Ehleringer, H. A. Mooney, O. E. Sala, and E. D. Schulze, “A global analysis of root distributions for terrestrial biomes,” Oecologia, vol. 108, no. 3, pp. 389–411, 1996. [45] D. J. Cooper, J. S. Sanderson, D. I. Stannard, and D. P. Groeneveld, “Effects of long-term water table drawdown on evapotranspiration and vegetation in an arid region phreatophyte community,” Journal of Hydrology, vol. 325, no. 1–4, pp. 21–34, 2006.

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